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<div class="title">SerializerTests.cpp</div>  </div>
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<a href="_serializer_tests_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>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#include &quot;../Serializer.hpp&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>&gt;</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>&gt;</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_types_utils_8hpp.xhtml">armnn/TypesUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_lstm_params_8hpp.xhtml">armnn/LstmParams.hpp</a>&gt;</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_quantized_lstm_params_8hpp.xhtml">armnn/QuantizedLstmParams.hpp</a>&gt;</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_i_deserializer_8hpp.xhtml">armnnDeserializer/IDeserializer.hpp</a>&gt;</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;</div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="preprocessor">#include &lt;random&gt;</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="preprocessor">#include &lt;vector&gt;</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;</div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="preprocessor">#include &lt;boost/test/unit_test.hpp&gt;</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;</div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="keyword">using</span> <a class="code" href="classarmnn_deserializer_1_1_i_deserializer.xhtml">armnnDeserializer::IDeserializer</a>;</div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;</div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;{</div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;</div><div class="line"><a name="l00025"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">   25</a></span>&#160;<span class="preprocessor">#define DECLARE_LAYER_VERIFIER_CLASS(name) \</span></div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="preprocessor">class name##LayerVerifier : public LayerVerifierBase \</span></div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">{ \</span></div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="preprocessor">public: \</span></div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;<span class="preprocessor">    name##LayerVerifier(const std::string&amp; layerName, \</span></div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="preprocessor">                        const std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos, \</span></div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="preprocessor">                        const std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos) \</span></div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="preprocessor">        : LayerVerifierBase(layerName, inputInfos, outputInfos) {} \</span></div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="preprocessor">\</span></div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="preprocessor">    void Visit##name##Layer(const armnn::IConnectableLayer* layer, const char* name) override \</span></div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;<span class="preprocessor">    { \</span></div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="preprocessor">        VerifyNameAndConnections(layer, name); \</span></div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="preprocessor">    } \</span></div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="preprocessor">};</span></div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;</div><div class="line"><a name="l00040"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">   40</a></span>&#160;<span class="preprocessor">#define DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(name) \</span></div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">class name##LayerVerifier : public LayerVerifierBaseWithDescriptor&lt;armnn::name##Descriptor&gt; \</span></div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">{ \</span></div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="preprocessor">public: \</span></div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="preprocessor">    name##LayerVerifier(const std::string&amp; layerName, \</span></div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">                        const std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos, \</span></div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="preprocessor">                        const std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos, \</span></div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="preprocessor">                        const armnn::name##Descriptor&amp; descriptor) \</span></div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="preprocessor">        : LayerVerifierBaseWithDescriptor&lt;armnn::name##Descriptor&gt;( \</span></div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="preprocessor">            layerName, inputInfos, outputInfos, descriptor) {} \</span></div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="preprocessor">\</span></div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;<span class="preprocessor">    void Visit##name##Layer(const armnn::IConnectableLayer* layer, \</span></div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;<span class="preprocessor">                            const armnn::name##Descriptor&amp; descriptor, \</span></div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;<span class="preprocessor">                            const char* name) override \</span></div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;<span class="preprocessor">    { \</span></div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;<span class="preprocessor">        VerifyNameAndConnections(layer, name); \</span></div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;<span class="preprocessor">        VerifyDescriptor(descriptor); \</span></div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;<span class="preprocessor">    } \</span></div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;<span class="preprocessor">};</span></div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;<span class="keyword">struct </span>DefaultLayerVerifierPolicy</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;{</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    <span class="keyword">static</span> <span class="keywordtype">void</span> Apply(<span class="keyword">const</span> std::string)</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;    {</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;        BOOST_TEST_MESSAGE(<span class="stringliteral">&quot;Unexpected layer found in network&quot;</span>);</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;        BOOST_TEST(<span class="keyword">false</span>);</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    }</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;};</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;<span class="keyword">class </span>LayerVerifierBase : <span class="keyword">public</span> <a class="code" href="classarmnn_1_1_layer_visitor_base.xhtml">armnn::LayerVisitorBase</a>&lt;DefaultLayerVerifierPolicy&gt;</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;{</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    LayerVerifierBase(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;                      <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;                      <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos)</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;    : m_LayerName(layerName)</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    , m_InputTensorInfos(inputInfos)</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    , m_OutputTensorInfos(outputInfos) {}</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    <span class="keywordtype">void</span> VisitInputLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*, <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a>, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override </span>{}</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    <span class="keywordtype">void</span> VisitOutputLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*, <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a>, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override </span>{}</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;<span class="keyword">protected</span>:</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    <span class="keywordtype">void</span> VerifyNameAndConnections(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer, <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;    {</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;        BOOST_TEST(name == m_LayerName.c_str());</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;        BOOST_TEST(layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>() == m_InputTensorInfos.size());</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;        BOOST_TEST(layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>() == m_OutputTensorInfos.size());</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; m_InputTensorInfos.size(); i++)</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        {</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;            <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* connectedOutput = layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>();</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;            <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(connectedOutput);</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;            <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&amp; connectedInfo = connectedOutput-&gt;<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;            BOOST_TEST(connectedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() == m_InputTensorInfos[i].GetShape());</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;            BOOST_TEST(</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;                <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(connectedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) == <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(m_InputTensorInfos[i].GetDataType()));</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;            BOOST_TEST(connectedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() == m_InputTensorInfos[i].GetQuantizationScale());</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;            BOOST_TEST(connectedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>() == m_InputTensorInfos[i].GetQuantizationOffset());</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;        }</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; m_OutputTensorInfos.size(); i++)</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;        {</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;            <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&amp; outputInfo = layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;            BOOST_TEST(outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() == m_OutputTensorInfos[i].GetShape());</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;            BOOST_TEST(</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;                <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) == <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(m_OutputTensorInfos[i].GetDataType()));</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;            BOOST_TEST(outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() == m_OutputTensorInfos[i].GetQuantizationScale());</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;            BOOST_TEST(outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>() == m_OutputTensorInfos[i].GetQuantizationOffset());</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;        }</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    }</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <span class="keywordtype">void</span> VerifyConstTensors(<span class="keyword">const</span> std::string&amp; tensorName,</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;                            <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>* expectedPtr,</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;                            <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>* actualPtr)</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    {</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;        <span class="keywordflow">if</span> (expectedPtr == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;        {</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;            BOOST_CHECK_MESSAGE(actualPtr == <span class="keyword">nullptr</span>, tensorName + <span class="stringliteral">&quot; should not exist&quot;</span>);</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;        }</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;        <span class="keywordflow">else</span></div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;        {</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;            BOOST_CHECK_MESSAGE(actualPtr != <span class="keyword">nullptr</span>, tensorName + <span class="stringliteral">&quot; should have been set&quot;</span>);</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;            <span class="keywordflow">if</span> (actualPtr != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;            {</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;                <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&amp; expectedInfo = expectedPtr-&gt;<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>();</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;                <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&amp; actualInfo = actualPtr-&gt;<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>();</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;                BOOST_CHECK_MESSAGE(expectedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() == actualInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(),</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;                                    tensorName + <span class="stringliteral">&quot; shapes don&#39;t match&quot;</span>);</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;                BOOST_CHECK_MESSAGE(</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;                        <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(expectedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) == <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(actualInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()),</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;                        tensorName + <span class="stringliteral">&quot; data types don&#39;t match&quot;</span>);</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;                BOOST_CHECK_MESSAGE(expectedPtr-&gt;<a class="code" href="classarmnn_1_1_base_tensor.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>() == actualPtr-&gt;<a class="code" href="classarmnn_1_1_base_tensor.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>(),</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;                                    tensorName + <span class="stringliteral">&quot; (GetNumBytes) data sizes do not match&quot;</span>);</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;                <span class="keywordflow">if</span> (expectedPtr-&gt;<a class="code" href="classarmnn_1_1_base_tensor.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>() == actualPtr-&gt;<a class="code" href="classarmnn_1_1_base_tensor.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>())</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;                {</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;                    <span class="comment">//check the data is identical</span></div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;                    <span class="keyword">const</span> <span class="keywordtype">char</span>* expectedData = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span>(expectedPtr-&gt;<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;                    <span class="keyword">const</span> <span class="keywordtype">char</span>* actualData = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span>(actualPtr-&gt;<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;                    <span class="keywordtype">bool</span> same = <span class="keyword">true</span>;</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;                    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; expectedPtr-&gt;<a class="code" href="classarmnn_1_1_base_tensor.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>(); ++i)</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;                    {</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;                        same = expectedData[i] == actualData[i];</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;                        <span class="keywordflow">if</span> (!same)</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;                        {</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;                            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;                        }</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;                    }</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;                    BOOST_CHECK_MESSAGE(same, tensorName + <span class="stringliteral">&quot; data does not match&quot;</span>);</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;                }</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;            }</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;        }</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    }</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    std::string m_LayerName;</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    std::vector&lt;armnn::TensorInfo&gt; m_InputTensorInfos;</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    std::vector&lt;armnn::TensorInfo&gt; m_OutputTensorInfos;</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;};</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Descriptor&gt;</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;<span class="keyword">class </span>LayerVerifierBaseWithDescriptor : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;{</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    LayerVerifierBaseWithDescriptor(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;                                    <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;                                    <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;                                    <span class="keyword">const</span> Descriptor&amp; descriptor)</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;        : LayerVerifierBase(layerName, inputInfos, outputInfos)</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;        , m_Descriptor(descriptor) {}</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;<span class="keyword">protected</span>:</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    <span class="keywordtype">void</span> VerifyDescriptor(<span class="keyword">const</span> Descriptor&amp; descriptor)</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    {</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor == m_Descriptor);</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    }</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    Descriptor m_Descriptor;</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;};</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;<span class="keywordtype">void</span> CompareConstTensorData(<span class="keyword">const</span> <span class="keywordtype">void</span>* data1, <span class="keyword">const</span> <span class="keywordtype">void</span>* data2, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements)</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;{</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    T typedData1 = <span class="keyword">static_cast&lt;</span>T<span class="keyword">&gt;</span>(data1);</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    T typedData2 = <span class="keyword">static_cast&lt;</span>T<span class="keyword">&gt;</span>(data2);</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(typedData1);</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(typedData2);</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; numElements; i++)</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;    {</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;        BOOST_TEST(typedData1[i] == typedData2[i]);</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;    }</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;}</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;<span class="keywordtype">void</span> CompareConstTensor(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; tensor1, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; tensor2)</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;{</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    BOOST_TEST(tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() == tensor2.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    BOOST_TEST(<a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) == <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(tensor2.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()));</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    <span class="keywordflow">switch</span> (tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>())</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    {</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;            CompareConstTensorData&lt;const float*&gt;(</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;                tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>(), tensor2.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>(), tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>:</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;            CompareConstTensorData&lt;const uint8_t*&gt;(</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;                tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>(), tensor2.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>(), tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>:</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;            CompareConstTensorData&lt;const int32_t*&gt;(</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;                tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>(), tensor2.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>(), tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;        <span class="keywordflow">default</span>:</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;            <span class="comment">// Note that Float16 is not yet implemented</span></div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;            BOOST_TEST_MESSAGE(<span class="stringliteral">&quot;Unexpected datatype&quot;</span>);</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;            BOOST_TEST(<span class="keyword">false</span>);</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    }</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;}</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> DeserializeNetwork(<span class="keyword">const</span> std::string&amp; serializerString)</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;{</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;    std::vector&lt;std::uint8_t&gt; <span class="keyword">const</span> serializerVector{serializerString.begin(), serializerString.end()};</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;    <span class="keywordflow">return</span> IDeserializer::Create()-&gt;CreateNetworkFromBinary(serializerVector);</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;}</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;std::string SerializeNetwork(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_network.xhtml">armnn::INetwork</a>&amp; network)</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;{</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;    <a class="code" href="classarmnn_serializer_1_1_serializer.xhtml">armnnSerializer::Serializer</a> <a class="code" href="namespacearmnn_serializer.xhtml">serializer</a>;</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    serializer.<a class="code" href="classarmnn_serializer_1_1_serializer.xhtml#a62dbb19d4776b489161b699f608f0150">Serialize</a>(network);</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;    std::stringstream stream;</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;    serializer.<a class="code" href="classarmnn_serializer_1_1_serializer.xhtml#af21f36069c661c4afa7221a305de80e0">SaveSerializedToStream</a>(stream);</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    std::string serializerString{stream.str()};</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    <span class="keywordflow">return</span> serializerString;</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;}</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> DataType&gt;</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;<span class="keyword">static</span> std::vector&lt;DataType&gt; GenerateRandomData(<span class="keywordtype">size_t</span> size)</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;{</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;    constexpr <span class="keywordtype">bool</span> isIntegerType = std::is_integral&lt;DataType&gt;::value;</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    <span class="keyword">using</span> Distribution =</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        <span class="keyword">typename</span> std::conditional&lt;isIntegerType,</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;                                  std::uniform_int_distribution&lt;DataType&gt;,</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;                                  std::uniform_real_distribution&lt;DataType&gt;&gt;::type;</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;    <span class="keyword">static</span> constexpr <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> lowerLimit = std::numeric_limits&lt;DataType&gt;::min();</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;    <span class="keyword">static</span> constexpr <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> upperLimit = std::numeric_limits&lt;DataType&gt;::max();</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;    <span class="keyword">static</span> Distribution distribution(lowerLimit, upperLimit);</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;    <span class="keyword">static</span> std::default_random_engine generator;</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;</div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;    std::vector&lt;DataType&gt; randomData(size);</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;    std::generate(randomData.begin(), randomData.end(), []() { <span class="keywordflow">return</span> distribution(generator); });</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    <span class="keywordflow">return</span> randomData;</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;}</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;} <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;<a class="code" href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(SerializerTests)</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;</div><div class="line"><a name="l00271"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">  271</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeAddition)</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;{</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Addition)</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;addition&quot;</span>);</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({1, 2, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> additionLayer = network-&gt;AddAdditionLayer(layerName.c_str());</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(additionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(additionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    additionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;    additionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;    AdditionLayerVerifier verifier(layerName, {tensorInfo, tensorInfo}, {tensorInfo});</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;}</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;</div><div class="line"><a name="l00299"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a0e876ee76e1b7b55c4a24cea29ee70ac">  299</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeArgMinMax)</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;{</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a374120de442fe42c26536bb4f1e2a5a1">ArgMinMax</a>)</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;argminmax&quot;</span>);</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 2, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>);</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    <a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">armnn::ArgMinMaxDescriptor</a> descriptor;</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::ArgMinMaxFunction::Max</a>;</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    descriptor.m_Axis = 1;</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> argMinMaxLayer = network-&gt;AddArgMinMaxLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(argMinMaxLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    argMinMaxLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    argMinMaxLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;    ArgMinMaxLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;}</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;</div><div class="line"><a name="l00329"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#add359ae172212d256d7024a16b577fa8">  329</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeBatchNormalization)</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;{</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;    <span class="keyword">using</span> Descriptor = <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a>;</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    <span class="keyword">class </span>BatchNormalizationLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor&lt;Descriptor&gt;</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    {</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    <span class="keyword">public</span>:</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;        BatchNormalizationLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;                                        <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;                                        <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;                                        <span class="keyword">const</span> Descriptor&amp; descriptor,</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;                                        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; mean,</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;                                        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; variance,</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;                                        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; beta,</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;                                        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; gamma)</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;            : LayerVerifierBaseWithDescriptor&lt;Descriptor&gt;(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;            , m_Mean(mean)</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;            , m_Variance(variance)</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;            , m_Beta(beta)</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;            , m_Gamma(gamma) {}</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;        <span class="keywordtype">void</span> VisitBatchNormalizationLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;                                          <span class="keyword">const</span> Descriptor&amp; descriptor,</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;                                          <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; mean,</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;                                          <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; variance,</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;                                          <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; beta,</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;                                          <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; gamma,</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;                                          <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;<span class="keyword">        </span>{</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;            VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;            VerifyDescriptor(descriptor);</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;            CompareConstTensor(mean, m_Mean);</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;            CompareConstTensor(variance, m_Variance);</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;            CompareConstTensor(beta, m_Beta);</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;            CompareConstTensor(gamma, m_Gamma);</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;        }</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    <span class="keyword">private</span>:</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Mean;</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Variance;</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Beta;</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Gamma;</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;    };</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;batchNormalization&quot;</span>);</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> meanInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> varianceInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> betaInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> gammaInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;    <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a> descriptor;</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = 0.0010000000475f;</div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;    descriptor.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;    std::vector&lt;float&gt; meanData({5.0});</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;    std::vector&lt;float&gt; varianceData({2.0});</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;    std::vector&lt;float&gt; betaData({1.0});</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;    std::vector&lt;float&gt; gammaData({0.0});</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> mean(meanInfo, meanData);</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> variance(varianceInfo, varianceData);</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> beta(betaInfo, betaData);</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> gamma(gammaInfo, gammaData);</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;</div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> batchNormalizationLayer =</div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;        network-&gt;AddBatchNormalizationLayer(descriptor, mean, variance, beta, gamma, layerName.c_str());</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;</div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(batchNormalizationLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;    batchNormalizationLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;</div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;    batchNormalizationLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;    BatchNormalizationLayerVerifier verifier(</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;        layerName, {inputInfo}, {outputInfo}, descriptor, mean, variance, beta, gamma);</div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;}</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;</div><div class="line"><a name="l00416"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a51de9ca6ac8a186f48cca59f392e4b50">  416</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeBatchToSpaceNd)</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;{</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a8746512fab5ec10c2c57800c66311ba7">BatchToSpaceNd</a>)</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;spaceToBatchNd&quot;</span>);</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({4, 1, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 1, 4, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;</div><div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;    <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a> desc;</div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;    desc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;    desc.m_BlockShape = {2, 2};</div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;    desc.m_Crops = {{0, 0}, {0, 0}};</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> batchToSpaceNdLayer = network-&gt;AddBatchToSpaceNdLayer(desc, layerName.c_str());</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(batchToSpaceNdLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;    batchToSpaceNdLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;    batchToSpaceNdLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;    BatchToSpaceNdLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;}</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;</div><div class="line"><a name="l00447"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a16dc6220342037f5a890ad7a912594e7">  447</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeComparison)</div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;{</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(Comparison)</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;comparison&quot;</span>);</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{2, 1, 2, 4};</div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo  = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;    <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a> descriptor(<a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca19bb0af2c3c530538cb41aff7f235b96">armnn::ComparisonOperation::NotEqual</a>);</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;</div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0     = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1     = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> comparisonLayer = network-&gt;AddComparisonLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer     = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(comparisonLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(comparisonLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;    comparisonLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;    comparisonLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;    ComparisonLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;}</div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;</div><div class="line"><a name="l00481"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a3c47c5c535712035fb962c91fffc3447">  481</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeConstant)</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;{</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;    <span class="keyword">class </span>ConstantLayerVerifier : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;    {</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;    <span class="keyword">public</span>:</div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;        ConstantLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;                              <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;                              <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;                              <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; layerInput)</div><div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;            : LayerVerifierBase(layerName, inputInfos, outputInfos)</div><div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;            , m_LayerInput(layerInput) {}</div><div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;</div><div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;        <span class="keywordtype">void</span> VisitConstantLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;                                <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; input,</div><div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;                                <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;<span class="keyword">        </span>{</div><div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;            VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;            CompareConstTensor(input, m_LayerInput);</div><div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;        }</div><div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;</div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;        <span class="keywordtype">void</span> VisitAdditionLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override </span>{}</div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;    <span class="keyword">private</span>:</div><div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_LayerInput;</div><div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;    };</div><div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;</div><div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;constant&quot;</span>);</div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;</div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;    std::vector&lt;float&gt; constantData = GenerateRandomData&lt;float&gt;(info.GetNumElements());</div><div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> constTensor(info, constantData);</div><div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;</div><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network(<a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* input = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* constant = network-&gt;AddConstantLayer(constTensor, layerName.c_str());</div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* add = network-&gt;AddAdditionLayer();</div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* output = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;</div><div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(add-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;    constant-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(add-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;    add-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;    constant-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;    add-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;</div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;</div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;    ConstantLayerVerifier verifier(layerName, {}, {info}, constTensor);</div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;}</div><div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;</div><div class="line"><a name="l00534"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a5356190bf530f061bfad94d3b5842e07">  534</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeConvolution2d)</div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;{</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;    <span class="keyword">using</span> Descriptor = <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a>;</div><div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;    <span class="keyword">class </span>Convolution2dLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor&lt;Descriptor&gt;</div><div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;    {</div><div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;    <span class="keyword">public</span>:</div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;        Convolution2dLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;                                   <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;                                   <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;                                   <span class="keyword">const</span> Descriptor&amp; descriptor,</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;                                   <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; weights,</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;                                   <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>&amp; biases)</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;            : LayerVerifierBaseWithDescriptor&lt;Descriptor&gt;(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;            , m_Weights(weights)</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;            , m_Biases(biases) {}</div><div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;</div><div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;        <span class="keywordtype">void</span> VisitConvolution2dLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;                                     <span class="keyword">const</span> Descriptor&amp; descriptor,</div><div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;                                     <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; weights,</div><div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;                                     <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>&amp; biases,</div><div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;                                     <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;<span class="keyword">        </span>{</div><div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;            VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;            VerifyDescriptor(descriptor);</div><div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;</div><div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;            <span class="comment">// check weights</span></div><div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;            CompareConstTensor(weights, m_Weights);</div><div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;</div><div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;            <span class="comment">// check biases</span></div><div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;            <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == descriptor.m_BiasEnabled);</div><div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;            <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == m_Biases.has_value());</div><div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;</div><div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;            <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() &amp;&amp; m_Biases.has_value())</div><div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;            {</div><div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;                CompareConstTensor(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(), m_Biases.value());</div><div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;            }</div><div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;        }</div><div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;</div><div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;    <span class="keyword">private</span>:</div><div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>                  m_Weights;</div><div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;        <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a> m_Biases;</div><div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;    };</div><div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;</div><div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;convolution2d&quot;</span>);</div><div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 5, 5, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;</div><div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;</div><div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;    std::vector&lt;float&gt; weightsData = GenerateRandomData&lt;float&gt;(weightsInfo.GetNumElements());</div><div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;</div><div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;    std::vector&lt;float&gt; biasesData = GenerateRandomData&lt;float&gt;(biasesInfo.GetNumElements());</div><div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;</div><div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;    <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>     = 1;</div><div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>    = 1;</div><div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>      = 1;</div><div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>   = 1;</div><div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>     = 2;</div><div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>     = 2;</div><div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a>   = 2;</div><div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a>   = 2;</div><div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;    descriptor.<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="l00600"></a><span class="lineno">  600</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>  = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;</div><div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer  = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> convLayer   =</div><div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;            network-&gt;AddConvolution2dLayer(descriptor,</div><div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;                                           weights,</div><div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;                                           <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>(biases),</div><div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;                                           layerName.c_str());</div><div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;</div><div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;    convLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;</div><div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;    convLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;</div><div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;</div><div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;    Convolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);</div><div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;}</div><div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;</div><div class="line"><a name="l00624"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a38de5ca76565a5326549aa88153f5aec">  624</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDepthToSpace)</div><div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;{</div><div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#ab023d9a7687e35c0f108458a094c1f56">DepthToSpace</a>)</div><div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;</div><div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;depthToSpace&quot;</span>);</div><div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;</div><div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1,  8, 4, 12 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 16, 8,  3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;</div><div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;    <a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::DepthToSpaceDescriptor</a> desc;</div><div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;    desc.<a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml#a6c6b8957f1e176867e5fb05b1a1a1486">m_BlockSize</a>  = 2;</div><div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;    desc.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;</div><div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer        = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> depthToSpaceLayer = network-&gt;AddDepthToSpaceLayer(desc, layerName.c_str());</div><div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer       = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;</div><div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(depthToSpaceLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;    depthToSpaceLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;</div><div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;    depthToSpaceLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;</div><div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;</div><div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;    DepthToSpaceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;}</div><div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;</div><div class="line"><a name="l00655"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a119be6a507d98bdd38a54db9f7036139">  655</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDepthwiseConvolution2d)</div><div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;{</div><div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;    <span class="keyword">using</span> Descriptor = <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a>;</div><div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;    <span class="keyword">class </span>DepthwiseConvolution2dLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor&lt;Descriptor&gt;</div><div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;    {</div><div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;    <span class="keyword">public</span>:</div><div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;        DepthwiseConvolution2dLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;                                            <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;                                            <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;                                            <span class="keyword">const</span> Descriptor&amp; descriptor,</div><div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;                                            <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; weights,</div><div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;                                            <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>&amp; biases) :</div><div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160; 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weights,</div><div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160;                                              <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>&amp; biases,</div><div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;                                              <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;<span class="keyword">        </span>{</div><div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;            VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;            VerifyDescriptor(descriptor);</div><div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;</div><div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;            <span class="comment">// check weights</span></div><div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160;            CompareConstTensor(weights, m_Weights);</div><div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;</div><div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;            <span class="comment">// check biases</span></div><div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;            <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == descriptor.m_BiasEnabled);</div><div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160;            <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == m_Biases.has_value());</div><div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;</div><div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;            <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() &amp;&amp; m_Biases.has_value())</div><div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;            {</div><div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;                CompareConstTensor(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(), m_Biases.value());</div><div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160;            }</div><div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;        }</div><div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;</div><div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;    <span class="keyword">private</span>:</div><div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>                      m_Weights;</div><div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;        <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>     m_Biases;</div><div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;    };</div><div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;</div><div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;depwiseConvolution2d&quot;</span>);</div><div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 5, 5, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 3, 3, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;</div><div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;</div><div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;    std::vector&lt;float&gt; weightsData = GenerateRandomData&lt;float&gt;(weightsInfo.GetNumElements());</div><div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160;</div><div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;    std::vector&lt;int32_t&gt; biasesData = GenerateRandomData&lt;int32_t&gt;(biasesInfo.GetNumElements());</div><div class="line"><a name="l00709"></a><span class="lineno">  709</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160;</div><div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;    <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>     = 1;</div><div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>    = 1;</div><div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>      = 1;</div><div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>   = 1;</div><div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>     = 2;</div><div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>     = 2;</div><div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a>   = 2;</div><div class="line"><a name="l00719"></a><span class="lineno">  719</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a>   = 2;</div><div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>  = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;</div><div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> depthwiseConvLayer =</div><div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;        network-&gt;AddDepthwiseConvolution2dLayer(descriptor,</div><div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;                                                weights,</div><div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160;                                                <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>(biases),</div><div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;                                                layerName.c_str());</div><div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160;</div><div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(depthwiseConvLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;    depthwiseConvLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;</div><div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;    depthwiseConvLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160;</div><div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;</div><div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;    DepthwiseConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);</div><div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;}</div><div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;</div><div class="line"><a name="l00745"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a20970cfb9b49e5080b90f605ae840761">  745</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDequantize)</div><div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;{</div><div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(<a class="code" href="namespacearmnn.xhtml#a855293b1be0581fb61ef6a1c5b027d0f">Dequantize</a>)</div><div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160;</div><div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;dequantize&quot;</span>);</div><div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>, 0.5f, 1);</div><div class="line"><a name="l00751"></a><span class="lineno">  751</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00752"></a><span class="lineno">  752</span>&#160;</div><div class="line"><a name="l00753"></a><span class="lineno">  753</span>&#160; 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   inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(dequantizeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160;    dequantizeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;</div><div class="line"><a name="l00761"></a><span class="lineno">  761</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00762"></a><span class="lineno">  762</span>&#160;    dequantizeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;</div><div class="line"><a name="l00764"></a><span class="lineno">  764</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00766"></a><span class="lineno">  766</span>&#160;</div><div class="line"><a name="l00767"></a><span class="lineno">  767</span>&#160;    DequantizeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo});</div><div class="line"><a name="l00768"></a><span class="lineno">  768</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00769"></a><span class="lineno">  769</span>&#160;}</div><div class="line"><a name="l00770"></a><span class="lineno">  770</span>&#160;</div><div class="line"><a name="l00771"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a855eea3f4f96815bb7a4cefde6791a3a">  771</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeDetectionPostProcess)</div><div class="line"><a name="l00772"></a><span class="lineno">  772</span>&#160;{</div><div class="line"><a name="l00773"></a><span class="lineno">  773</span>&#160;    <span class="keyword">using</span> Descriptor = <a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml">armnn::DetectionPostProcessDescriptor</a>;</div><div class="line"><a name="l00774"></a><span class="lineno">  774</span>&#160;    <span class="keyword">class </span>DetectionPostProcessLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor&lt;Descriptor&gt;</div><div class="line"><a name="l00775"></a><span class="lineno">  775</span>&#160;    {</div><div class="line"><a name="l00776"></a><span class="lineno">  776</span>&#160;    <span class="keyword">public</span>:</div><div class="line"><a name="l00777"></a><span class="lineno">  777</span>&#160;        DetectionPostProcessLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l00778"></a><span class="lineno">  778</span>&#160;                                          <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l00779"></a><span class="lineno">  779</span>&#160;                                          <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l00780"></a><span class="lineno">  780</span>&#160;                                          <span class="keyword">const</span> Descriptor&amp; descriptor,</div><div class="line"><a name="l00781"></a><span class="lineno">  781</span>&#160;                                          <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; <a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#ac0981848e4ae57729f14f72bd4caa9f8">anchors</a>)</div><div class="line"><a name="l00782"></a><span class="lineno">  782</span>&#160;            : LayerVerifierBaseWithDescriptor&lt;Descriptor&gt;(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l00783"></a><span class="lineno">  783</span>&#160;            , m_Anchors(anchors) {}</div><div class="line"><a name="l00784"></a><span class="lineno">  784</span>&#160;</div><div class="line"><a name="l00785"></a><span class="lineno">  785</span>&#160;        <span class="keywordtype">void</span> VisitDetectionPostProcessLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00786"></a><span class="lineno">  786</span>&#160;                                            <span class="keyword">const</span> Descriptor&amp; descriptor,</div><div class="line"><a name="l00787"></a><span class="lineno">  787</span>&#160;                                            <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; anchors,</div><div class="line"><a name="l00788"></a><span class="lineno">  788</span>&#160;                                            <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l00789"></a><span class="lineno">  789</span>&#160;<span class="keyword">        </span>{</div><div class="line"><a name="l00790"></a><span class="lineno">  790</span>&#160;            VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00791"></a><span class="lineno">  791</span>&#160;            VerifyDescriptor(descriptor);</div><div class="line"><a name="l00792"></a><span class="lineno">  792</span>&#160;</div><div class="line"><a name="l00793"></a><span class="lineno">  793</span>&#160;            CompareConstTensor(anchors, m_Anchors);</div><div class="line"><a name="l00794"></a><span class="lineno">  794</span>&#160;        }</div><div class="line"><a name="l00795"></a><span class="lineno">  795</span>&#160;</div><div class="line"><a name="l00796"></a><span class="lineno">  796</span>&#160;    <span class="keyword">private</span>:</div><div class="line"><a name="l00797"></a><span class="lineno">  797</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Anchors;</div><div class="line"><a name="l00798"></a><span class="lineno">  798</span>&#160;    };</div><div class="line"><a name="l00799"></a><span class="lineno">  799</span>&#160;</div><div class="line"><a name="l00800"></a><span class="lineno">  800</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;detectionPostProcess&quot;</span>);</div><div class="line"><a name="l00801"></a><span class="lineno">  801</span>&#160;</div><div class="line"><a name="l00802"></a><span class="lineno">  802</span>&#160;    <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt; inputInfos({</div><div class="line"><a name="l00803"></a><span class="lineno">  803</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 6, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>),</div><div class="line"><a name="l00804"></a><span class="lineno">  804</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 6, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>)</div><div class="line"><a name="l00805"></a><span class="lineno">  805</span>&#160;    });</div><div class="line"><a name="l00806"></a><span class="lineno">  806</span>&#160;</div><div class="line"><a name="l00807"></a><span class="lineno">  807</span>&#160;    <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt; outputInfos({</div><div class="line"><a name="l00808"></a><span class="lineno">  808</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>),</div><div class="line"><a name="l00809"></a><span class="lineno">  809</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>),</div><div class="line"><a name="l00810"></a><span class="lineno">  810</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>),</div><div class="line"><a name="l00811"></a><span class="lineno">  811</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>)</div><div class="line"><a name="l00812"></a><span class="lineno">  812</span>&#160;    });</div><div class="line"><a name="l00813"></a><span class="lineno">  813</span>&#160;</div><div class="line"><a name="l00814"></a><span class="lineno">  814</span>&#160;    <a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml">armnn::DetectionPostProcessDescriptor</a> descriptor;</div><div class="line"><a name="l00815"></a><span class="lineno">  815</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7ed9bc7c26df67d274d5dd4cd83adf0f">m_UseRegularNms</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00816"></a><span class="lineno">  816</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">m_MaxDetections</a> = 3;</div><div class="line"><a name="l00817"></a><span class="lineno">  817</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a9ae2c9796692ebeafe19a4d3f09c8ea8">m_MaxClassesPerDetection</a> = 1;</div><div class="line"><a name="l00818"></a><span class="lineno">  818</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7e2f87544b8bc7e497e1dec8d3ca4055">m_DetectionsPerClass</a> =1;</div><div class="line"><a name="l00819"></a><span class="lineno">  819</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a4392dd6b4862cc9cf95ae8f1001ba592">m_NmsScoreThreshold</a> = 0.0;</div><div class="line"><a name="l00820"></a><span class="lineno">  820</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">m_NmsIouThreshold</a> = 0.5;</div><div class="line"><a name="l00821"></a><span class="lineno">  821</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a3a04b0ccee4bb2f21721ee5045e83df4">m_NumClasses</a> = 2;</div><div class="line"><a name="l00822"></a><span class="lineno">  822</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7a2156ec7d9c012ce00bbcc6afcb9028">m_ScaleY</a> = 10.0;</div><div class="line"><a name="l00823"></a><span class="lineno">  823</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae64523937ea910030ad66fee6fddd51f">m_ScaleX</a> = 10.0;</div><div class="line"><a name="l00824"></a><span class="lineno">  824</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#aa61510cbd529870182e918ac6e8b9d72">m_ScaleH</a> = 5.0;</div><div class="line"><a name="l00825"></a><span class="lineno">  825</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ab509802c659de19929f18bad14a35c58">m_ScaleW</a> = 5.0;</div><div class="line"><a name="l00826"></a><span class="lineno">  826</span>&#160;</div><div class="line"><a name="l00827"></a><span class="lineno">  827</span>&#160; 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       0.5f, 10.5f, 1.0f, 1.0f,</div><div class="line"><a name="l00834"></a><span class="lineno">  834</span>&#160;        0.5f, 100.5f, 1.0f, 1.0f</div><div class="line"><a name="l00835"></a><span class="lineno">  835</span>&#160;    });</div><div class="line"><a name="l00836"></a><span class="lineno">  836</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> <a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#ac0981848e4ae57729f14f72bd4caa9f8">anchors</a>(<a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#a358cb7cd3c0647b25be049fd734b8c22">anchorsInfo</a>, anchorsData);</div><div class="line"><a name="l00837"></a><span class="lineno">  837</span>&#160;</div><div class="line"><a name="l00838"></a><span class="lineno">  838</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00839"></a><span class="lineno">  839</span>&#160; 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       inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(detectionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(i));</div><div class="line"><a name="l00846"></a><span class="lineno">  846</span>&#160;        inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfos[i]);</div><div class="line"><a name="l00847"></a><span class="lineno">  847</span>&#160;    }</div><div class="line"><a name="l00848"></a><span class="lineno">  848</span>&#160;</div><div class="line"><a name="l00849"></a><span class="lineno">  849</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; 4; i++)</div><div class="line"><a name="l00850"></a><span class="lineno">  850</span>&#160;    {</div><div class="line"><a name="l00851"></a><span class="lineno">  851</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(static_cast&lt;int&gt;(i));</div><div class="line"><a name="l00852"></a><span class="lineno">  852</span>&#160;        detectionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00853"></a><span class="lineno">  853</span>&#160;        detectionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfos[i]);</div><div class="line"><a name="l00854"></a><span class="lineno">  854</span>&#160;    }</div><div class="line"><a name="l00855"></a><span class="lineno">  855</span>&#160;</div><div class="line"><a name="l00856"></a><span class="lineno">  856</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00857"></a><span class="lineno">  857</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00858"></a><span class="lineno">  858</span>&#160;</div><div class="line"><a name="l00859"></a><span class="lineno">  859</span>&#160;    DetectionPostProcessLayerVerifier verifier(layerName, inputInfos, outputInfos, descriptor, anchors);</div><div class="line"><a name="l00860"></a><span class="lineno">  860</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00861"></a><span class="lineno">  861</span>&#160;}</div><div class="line"><a name="l00862"></a><span class="lineno">  862</span>&#160;</div><div class="line"><a name="l00863"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a3472d2c268b61fb7d623163c7d828c80">  863</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDivision)</div><div class="line"><a name="l00864"></a><span class="lineno">  864</span>&#160;{</div><div class="line"><a name="l00865"></a><span class="lineno">  865</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Division)</div><div class="line"><a name="l00866"></a><span class="lineno">  866</span>&#160;</div><div class="line"><a name="l00867"></a><span class="lineno">  867</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;division&quot;</span>);</div><div class="line"><a name="l00868"></a><span class="lineno">  868</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00869"></a><span class="lineno">  869</span>&#160;</div><div class="line"><a name="l00870"></a><span class="lineno">  870</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00871"></a><span class="lineno">  871</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00872"></a><span class="lineno">  872</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l00873"></a><span class="lineno">  873</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> divisionLayer = network-&gt;AddDivisionLayer(layerName.c_str());</div><div class="line"><a name="l00874"></a><span class="lineno">  874</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00875"></a><span class="lineno">  875</span>&#160;</div><div class="line"><a name="l00876"></a><span class="lineno">  876</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(divisionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00877"></a><span class="lineno">  877</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(divisionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00878"></a><span class="lineno">  878</span>&#160;    divisionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00879"></a><span class="lineno">  879</span>&#160;</div><div class="line"><a name="l00880"></a><span class="lineno">  880</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00881"></a><span class="lineno">  881</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00882"></a><span class="lineno">  882</span>&#160;    divisionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00883"></a><span class="lineno">  883</span>&#160;</div><div class="line"><a name="l00884"></a><span class="lineno">  884</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00885"></a><span class="lineno">  885</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00886"></a><span class="lineno">  886</span>&#160;</div><div class="line"><a name="l00887"></a><span class="lineno">  887</span>&#160;    DivisionLayerVerifier verifier(layerName, {info, info}, {info});</div><div class="line"><a name="l00888"></a><span class="lineno">  888</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00889"></a><span class="lineno">  889</span>&#160;}</div><div class="line"><a name="l00890"></a><span class="lineno">  890</span>&#160;</div><div class="line"><a name="l00891"></a><span class="lineno">  891</span>&#160;<span class="keyword">class </span>EqualLayerVerifier : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l00892"></a><span class="lineno">  892</span>&#160;{</div><div class="line"><a name="l00893"></a><span class="lineno">  893</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00894"></a><span class="lineno">  894</span>&#160;    EqualLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l00895"></a><span class="lineno">  895</span>&#160;                       <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l00896"></a><span class="lineno">  896</span>&#160;                       <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos)</div><div class="line"><a name="l00897"></a><span class="lineno">  897</span>&#160;        : LayerVerifierBase(layerName, inputInfos, outputInfos) {}</div><div class="line"><a name="l00898"></a><span class="lineno">  898</span>&#160;</div><div class="line"><a name="l00899"></a><span class="lineno">  899</span>&#160;    <span class="keywordtype">void</span> VisitComparisonLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00900"></a><span class="lineno">  900</span>&#160;                              <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a>&amp; descriptor,</div><div class="line"><a name="l00901"></a><span class="lineno">  901</span>&#160;                              <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l00902"></a><span class="lineno">  902</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l00903"></a><span class="lineno">  903</span>&#160;        VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00904"></a><span class="lineno">  904</span>&#160;        <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor.<a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml#a865dc4f43cb0ff01a1dcf78036912fd1">m_Operation</a> == <a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58caf5f286e73bda105e538310b3190f75c5">armnn::ComparisonOperation::Equal</a>);</div><div class="line"><a name="l00905"></a><span class="lineno">  905</span>&#160;    }</div><div class="line"><a name="l00906"></a><span class="lineno">  906</span>&#160;</div><div class="line"><a name="l00907"></a><span class="lineno">  907</span>&#160;    <span class="keywordtype">void</span> VisitEqualLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override</span></div><div class="line"><a name="l00908"></a><span class="lineno">  908</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l00909"></a><span class="lineno">  909</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">&quot;EqualLayer should have translated to ComparisonLayer&quot;</span>);</div><div class="line"><a name="l00910"></a><span class="lineno">  910</span>&#160;    }</div><div class="line"><a name="l00911"></a><span class="lineno">  911</span>&#160;};</div><div class="line"><a name="l00912"></a><span class="lineno">  912</span>&#160;</div><div class="line"><a name="l00913"></a><span class="lineno">  913</span>&#160;<span class="comment">// NOTE: Until the deprecated AddEqualLayer disappears this test checks that calling</span></div><div class="line"><a name="l00914"></a><span class="lineno">  914</span>&#160;<span class="comment">//       AddEqualLayer places a ComparisonLayer into the serialized format and that</span></div><div class="line"><a name="l00915"></a><span class="lineno">  915</span>&#160;<span class="comment">//       when this deserialises we have a ComparisonLayer</span></div><div class="line"><a name="l00916"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a6a14fa535c1751752fdbd0725bc7ad3e">  916</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeEqual)</div><div class="line"><a name="l00917"></a><span class="lineno">  917</span>&#160;{</div><div class="line"><a name="l00918"></a><span class="lineno">  918</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;equal&quot;</span>);</div><div class="line"><a name="l00919"></a><span class="lineno">  919</span>&#160;</div><div class="line"><a name="l00920"></a><span class="lineno">  920</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{2, 1, 2, 4};</div><div class="line"><a name="l00921"></a><span class="lineno">  921</span>&#160;</div><div class="line"><a name="l00922"></a><span class="lineno">  922</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo  = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00923"></a><span class="lineno">  923</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l00924"></a><span class="lineno">  924</span>&#160;</div><div class="line"><a name="l00925"></a><span class="lineno">  925</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00926"></a><span class="lineno">  926</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00927"></a><span class="lineno">  927</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l00928"></a><span class="lineno">  928</span>&#160;    <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l00929"></a><span class="lineno">  929</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> equalLayer = network-&gt;AddEqualLayer(layerName.c_str());</div><div class="line"><a name="l00930"></a><span class="lineno">  930</span>&#160;    <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l00931"></a><span class="lineno">  931</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00932"></a><span class="lineno">  932</span>&#160;</div><div class="line"><a name="l00933"></a><span class="lineno">  933</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(equalLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00934"></a><span class="lineno">  934</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(equalLayer-&gt;GetInputSlot(1));</div><div class="line"><a name="l00935"></a><span class="lineno">  935</span>&#160;    equalLayer-&gt;GetOutputSlot(0).Connect(outputLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00936"></a><span class="lineno">  936</span>&#160;</div><div class="line"><a name="l00937"></a><span class="lineno">  937</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00938"></a><span class="lineno">  938</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00939"></a><span class="lineno">  939</span>&#160;    equalLayer-&gt;GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l00940"></a><span class="lineno">  940</span>&#160;</div><div class="line"><a name="l00941"></a><span class="lineno">  941</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00942"></a><span class="lineno">  942</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00943"></a><span class="lineno">  943</span>&#160;</div><div class="line"><a name="l00944"></a><span class="lineno">  944</span>&#160;    EqualLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo });</div><div class="line"><a name="l00945"></a><span class="lineno">  945</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l00946"></a><span class="lineno">  946</span>&#160;}</div><div class="line"><a name="l00947"></a><span class="lineno">  947</span>&#160;</div><div class="line"><a name="l00948"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a50830e0b70c8a84af0b27f0dcf4b3389">  948</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(EnsureEqualBackwardCompatibility)</div><div class="line"><a name="l00949"></a><span class="lineno">  949</span>&#160;{</div><div class="line"><a name="l00950"></a><span class="lineno">  950</span>&#160;    <span class="comment">// The hex data below is a flat buffer containing a simple network with two inputs,</span></div><div class="line"><a name="l00951"></a><span class="lineno">  951</span>&#160; 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   };</div><div class="line"><a name="l00995"></a><span class="lineno">  995</span>&#160;</div><div class="line"><a name="l00996"></a><span class="lineno">  996</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(std::string(equalModel.begin(), equalModel.end()));</div><div class="line"><a name="l00997"></a><span class="lineno">  997</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00998"></a><span class="lineno">  998</span>&#160;</div><div class="line"><a name="l00999"></a><span class="lineno">  999</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{ 2, 1, 2, 4 };</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>&#160;</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>&#160; 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   <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({4,4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>&#160;</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>&#160; 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   floorLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>&#160;</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>&#160;    floorLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>&#160;</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>&#160;</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>&#160;    FloorLayerVerifier verifier(layerName, {info}, {info});</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>&#160;}</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>&#160;</div><div class="line"><a name="l01033"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a0d3a0736e9d014001bd37e232c54ff48"> 1033</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeFullyConnected)</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>&#160;{</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>&#160;    <span class="keyword">using</span> Descriptor = <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a>;</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>&#160;    <span class="keyword">class </span>FullyConnectedLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor&lt;Descriptor&gt;</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>&#160;    {</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>&#160;    <span class="keyword">public</span>:</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>&#160;        FullyConnectedLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>&#160;                                    <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>&#160;                                    <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>&#160;                                    <span class="keyword">const</span> Descriptor&amp; descriptor,</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>&#160;                                    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; weight,</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>&#160;                                    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>&amp; bias)</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>&#160;            : LayerVerifierBaseWithDescriptor&lt;Descriptor&gt;(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>&#160;            , m_Weight(weight)</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>&#160;            , m_Bias(bias) {}</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>&#160;</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>&#160;        <span class="keywordtype">void</span> VisitFullyConnectedLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>&#160;                                      <span class="keyword">const</span> Descriptor&amp; descriptor,</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>&#160;                                      <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; weight,</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>&#160;                                      <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>&amp; bias,</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>&#160;                                      <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>&#160;<span class="keyword">        </span>{</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>&#160;            VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>&#160;            VerifyDescriptor(descriptor);</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>&#160;</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>&#160;            CompareConstTensor(weight, m_Weight);</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>&#160;</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>&#160;            BOOST_TEST(bias.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == descriptor.m_BiasEnabled);</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>&#160;            BOOST_TEST(bias.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == m_Bias.has_value());</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>&#160;</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>&#160;            <span class="keywordflow">if</span> (bias.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() &amp;&amp; m_Bias.has_value())</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>&#160;            {</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>&#160;                CompareConstTensor(bias.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(), m_Bias.value());</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>&#160;            }</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>&#160;        }</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>&#160;</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>&#160;    <span class="keyword">private</span>:</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Weight;</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>&#160;        <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a> m_Bias;</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>&#160;    };</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>&#160;</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;fullyConnected&quot;</span>);</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 2, 5, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>&#160;</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 5, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>&#160;    std::vector&lt;float&gt; weightsData = GenerateRandomData&lt;float&gt;(weightsInfo.GetNumElements());</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>&#160;    std::vector&lt;float&gt; biasesData  = GenerateRandomData&lt;float&gt;(biasesInfo.GetNumElements());</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>&#160;</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>&#160;    <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> descriptor;</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>&#160;    descriptor.<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="l01087"></a><span class="lineno"> 1087</span>&#160;    descriptor.<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="l01088"></a><span class="lineno"> 1088</span>&#160;</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> fullyConnectedLayer =</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>&#160;        network-&gt;AddFullyConnectedLayer(descriptor,</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>&#160;                                        weights,</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>&#160;                                        <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>(biases),</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>&#160;                                        layerName.c_str());</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>&#160; 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   inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>&#160;    fullyConnectedLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>&#160;</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>&#160;</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>&#160;    FullyConnectedLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>&#160;}</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>&#160;</div><div class="line"><a name="l01111"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a5174c1e8962eda5aab37f17d72506c75"> 1111</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeGather)</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>&#160;{</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>&#160;    <span class="keyword">class </span>GatherLayerVerifier : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>&#160;    {</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>&#160;    <span class="keyword">public</span>:</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>&#160;        GatherLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>&#160;                            <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>&#160;                            <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos)</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>&#160;            : LayerVerifierBase(layerName, inputInfos, outputInfos) {}</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>&#160;</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>&#160; 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                               <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp;,</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>&#160;                                <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override </span>{}</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>&#160;    };</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>&#160;</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;gather&quot;</span>);</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> paramsInfo({ 8 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>);</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>);</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> indicesInfo({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>);</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>&#160;</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>&#160;    paramsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(1.0f);</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>&#160;    paramsInfo.SetQuantizationOffset(0);</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>&#160;    outputInfo.SetQuantizationScale(1.0f);</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>&#160;    outputInfo.SetQuantizationOffset(0);</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>&#160;</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>&#160;    <span class="keyword">const</span> std::vector&lt;int32_t&gt;&amp; indicesData = {7, 6, 5};</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>&#160;</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> constantLayer =</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>&#160;            network-&gt;AddConstantLayer(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(indicesInfo, indicesData));</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> gatherLayer = network-&gt;AddGatherLayer(layerName.c_str());</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>&#160;</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(gatherLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>&#160;    constantLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(gatherLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>&#160;    gatherLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>&#160;</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(paramsInfo);</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>&#160;    constantLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(indicesInfo);</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>&#160;    gatherLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>&#160;</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>&#160;</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>&#160;    GatherLayerVerifier verifier(layerName, {paramsInfo, indicesInfo}, {outputInfo});</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>&#160;}</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>&#160;</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>&#160;<span class="keyword">class </span>GreaterLayerVerifier : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>&#160;{</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>&#160;    GreaterLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>&#160;                         <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>&#160;                         <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos)</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>&#160;        : LayerVerifierBase(layerName, inputInfos, outputInfos) {}</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>&#160;</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>&#160;    <span class="keywordtype">void</span> VisitComparisonLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>&#160;                              <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a>&amp; descriptor,</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>&#160;                              <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>&#160;        VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>&#160;        <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor.<a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml#a865dc4f43cb0ff01a1dcf78036912fd1">m_Operation</a> == <a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca8768a6821cd735aea4f5b0df88c1fc6a">armnn::ComparisonOperation::Greater</a>);</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>&#160;    }</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>&#160;</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>&#160;    <span class="keywordtype">void</span> VisitGreaterLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override</span></div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">&quot;GreaterLayer should have translated to ComparisonLayer&quot;</span>);</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>&#160;    }</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>&#160;};</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>&#160;</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>&#160;<span class="comment">// NOTE: Until the deprecated AddGreaterLayer disappears this test checks that calling</span></div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>&#160;<span class="comment">//       AddGreaterLayer places a ComparisonLayer into the serialized format and that</span></div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>&#160;<span class="comment">//       when this deserialises we have a ComparisonLayer</span></div><div class="line"><a name="l01190"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a461a321aa5b80473430a18a899c801f7"> 1190</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeGreater)</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>&#160;{</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;greater&quot;</span>);</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>&#160;</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{2, 1, 2, 4};</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>&#160;</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo  = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>&#160;</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>&#160;    <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> equalLayer = network-&gt;AddGreaterLayer(layerName.c_str());</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>&#160;    <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>&#160;</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(equalLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(equalLayer-&gt;GetInputSlot(1));</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>&#160;    equalLayer-&gt;GetOutputSlot(0).Connect(outputLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>&#160;</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>&#160;    equalLayer-&gt;GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>&#160;</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>&#160;</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>&#160; 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   <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;instanceNormalization&quot;</span>);</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 2, 1, 5 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>&#160;</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>&#160;    <a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml">armnn::InstanceNormalizationDescriptor</a> descriptor;</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a5e078fd505aef7bccaa05c8058e096cc">m_Gamma</a>      = 1.1f;</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>&#160; 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   <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#ac52e04c0e349e25bcdaa72c27395ef8f">LogSoftmax</a>)</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>&#160;</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;log_softmax&quot;</span>);</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({1, 10}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>&#160;</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>&#160;    <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::LogSoftmaxDescriptor</a> descriptor;</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = 1.0f;</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>&#160;    descriptor.m_Axis = -1;</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>&#160;</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer      = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>&#160; 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   logSoftmaxLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>&#160;</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>&#160;    logSoftmaxLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>&#160;</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>&#160;</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>&#160;    LogSoftmaxLayerVerifier verifier(layerName, {info}, {info}, descriptor);</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>&#160;}</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>&#160;</div><div class="line"><a name="l01428"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aeedeb69141be5280c53e327a2ac76320"> 1428</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeMaximum)</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>&#160;{</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Maximum)</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>&#160;</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;maximum&quot;</span>);</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>&#160;</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> maximumLayer = network-&gt;AddMaximumLayer(layerName.c_str());</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>&#160;</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(maximumLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(maximumLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>&#160;    maximumLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>&#160;</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>&#160; 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   <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>&#160;</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>&#160;    MaximumLayerVerifier verifier(layerName, {info, info}, {info});</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>&#160;}</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>&#160;</div><div class="line"><a name="l01456"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a4da2b73764b4e976afb82b8864b99be8"> 1456</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeMean)</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>&#160;{</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a165ae372a7f67cad64ef3395d30122ce">Mean</a>)</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>&#160;</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;mean&quot;</span>);</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 1, 3, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 1, 1, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>&#160;</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>&#160; 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   meanLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>&#160;</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>&#160;    meanLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>&#160;</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>&#160;</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>&#160;    MeanLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>&#160;}</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>&#160;</div><div class="line"><a name="l01486"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aa5b91e9c6d7ba20294ff5416969a85cf"> 1486</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeMerge)</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>&#160;{</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Merge)</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>&#160;</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;merge&quot;</span>);</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>&#160;</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> mergeLayer = network-&gt;AddMergeLayer(layerName.c_str());</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>&#160;</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(mergeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(mergeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>&#160;    mergeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>&#160;</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>&#160;    mergeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span>&#160;</div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>&#160;</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>&#160;    MergeLayerVerifier verifier(layerName, {info, info}, {info});</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>&#160;}</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>&#160;</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>&#160;<span class="keyword">class </span>MergerLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor&lt;armnn::OriginsDescriptor&gt;</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>&#160;{</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>&#160;    MergerLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>&#160;                        <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>&#160;                        <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>&#160;                        <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a>&amp; descriptor)</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>&#160;        : LayerVerifierBaseWithDescriptor&lt;armnn::OriginsDescriptor&gt;(layerName, inputInfos, outputInfos, descriptor) {}</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>&#160;</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>&#160; 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   <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;merger&quot;</span>);</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({4, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>&#160;</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>&#160;    <span class="keyword">const</span> std::vector&lt;armnn::TensorShape&gt; shapes({inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()});</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>&#160;</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>&#160;    <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a> descriptor =</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a>(shapes.begin(), shapes.end(), 0);</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>&#160;</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>&#160; 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   <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a> descriptor =</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>&#160;            <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a>(shapes.begin(), shapes.end(), 0);</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span>&#160;</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>&#160;    MergerLayerVerifier verifier(<span class="stringliteral">&quot;merger&quot;</span>, { inputInfo, inputInfo }, { outputInfo }, descriptor);</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>&#160;}</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>&#160;</div><div class="line"><a name="l01646"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a8560bbe4155b6f639feea42ca759ceb8"> 1646</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeConcat)</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>&#160;{</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;concat&quot;</span>);</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({4, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>&#160;</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>&#160;    <span class="keyword">const</span> std::vector&lt;armnn::TensorShape&gt; shapes({inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()});</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>&#160;</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>&#160;    <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a> descriptor =</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a>(shapes.begin(), shapes.end(), 0);</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>&#160;</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayerOne = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayerTwo = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> concatLayer = network-&gt;AddConcatLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span>&#160;</div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>&#160;    inputLayerOne-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>&#160;    inputLayerTwo-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>&#160;    concatLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>&#160;</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>&#160;    inputLayerOne-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>&#160;    inputLayerTwo-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>&#160;    concatLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>&#160;</div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>&#160;    std::string concatLayerNetwork = SerializeNetwork(*network);</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(concatLayerNetwork);</div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>&#160;</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>&#160; 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   <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Minimum)</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>&#160;</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;minimum&quot;</span>);</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>&#160;</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> minimumLayer = network-&gt;AddMinimumLayer(layerName.c_str());</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>&#160;</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>&#160; 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   <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Multiplication)</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>&#160;</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;multiplication&quot;</span>);</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>&#160;</div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> multiplicationLayer = network-&gt;AddMultiplicationLayer(layerName.c_str());</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>&#160;</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(multiplicationLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(multiplicationLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>&#160;    multiplicationLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>&#160;</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>&#160;    multiplicationLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>&#160;</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>&#160;</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>&#160;    MultiplicationLayerVerifier verifier(layerName, {info, info}, {info});</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>&#160;}</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>&#160;</div><div class="line"><a name="l01737"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a2bb05baf0128ccdc37d28da84f8d5986"> 1737</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializePrelu)</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>&#160;{</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Prelu)</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>&#160;</div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;prelu&quot;</span>);</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>&#160;</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo ({ 4, 1, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> alphaTensorInfo ({ 5, 4, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 5, 4, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>&#160;</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> alphaLayer = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> preluLayer = network-&gt;AddPreluLayer(layerName.c_str());</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>&#160;</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(preluLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>&#160;    alphaLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(preluLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>&#160;    preluLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>&#160;</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>&#160;    alphaLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(alphaTensorInfo);</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>&#160;    preluLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>&#160;</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>&#160;</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>&#160;    PreluLayerVerifier verifier(layerName, {inputTensorInfo, alphaTensorInfo}, {outputTensorInfo});</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>&#160;}</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>&#160;</div><div class="line"><a name="l01768"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#afa5c407820579e1e5c0c21a5e189fc15"> 1768</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeNormalization)</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>&#160;{</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(Normalization)</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>&#160;</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;normalization&quot;</span>);</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({2, 1, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>&#160;</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>&#160;    <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a> desc;</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>&#160;    desc.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>&#160;    desc.m_NormSize = 3;</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>&#160;    desc.m_Alpha = 1;</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>&#160;    desc.m_Beta = 1;</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>&#160;    desc.m_K = 1;</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>&#160;</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> normalizationLayer = network-&gt;AddNormalizationLayer(desc, layerName.c_str());</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>&#160;</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(normalizationLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>&#160; 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   PadLayerVerifier verifier(<span class="stringliteral">&quot;pad&quot;</span>, { inputInfo }, { outputInfo }, descriptor);</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>&#160;}</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>&#160;</div><div class="line"><a name="l01882"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a4959da03b99bcdf40acb442ca9b1752f"> 1882</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializePermute)</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span>&#160;{</div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">Permute</a>)</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>&#160;</div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;permute&quot;</span>);</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({4, 3, 2, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({1, 2, 3, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>&#160;</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>&#160;    <a class="code" href="structarmnn_1_1_permute_descriptor.xhtml">armnn::PermuteDescriptor</a> descriptor(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>({3, 2, 1, 0}));</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>&#160;</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> permuteLayer = network-&gt;AddPermuteLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>&#160;</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(permuteLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>&#160;    permuteLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>&#160;</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>&#160;    permuteLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>&#160;</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>&#160;</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>&#160;    PermuteLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>&#160;}</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>&#160;</div><div class="line"><a name="l01910"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a1684386a1de76091bfb6407d82db04f7"> 1910</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializePooling2d)</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>&#160;{</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#ae2e93e304cf516841c521e3eaee025cd">Pooling2d</a>)</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>&#160;</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;pooling2d&quot;</span>);</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 2, 2, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 1, 1, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>&#160;</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>&#160;    <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>&#160;    desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>          = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>&#160;    desc.m_PadTop              = 0;</div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>&#160;    desc.m_PadBottom           = 0;</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>&#160;    desc.m_PadLeft             = 0;</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>&#160;    desc.m_PadRight            = 0;</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>&#160;    desc.m_PoolType            = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>&#160;    desc.m_OutputShapeRounding = <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>&#160;    desc.m_PaddingMethod       = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>&#160;    desc.m_PoolHeight          = 2;</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>&#160;    desc.m_PoolWidth           = 2;</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>&#160;    desc.m_StrideX             = 2;</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>&#160;    desc.m_StrideY             = 2;</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>&#160;</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> pooling2dLayer = network-&gt;AddPooling2dLayer(desc, layerName.c_str());</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>&#160;</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(pooling2dLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>&#160;    pooling2dLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>&#160;</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>&#160;    pooling2dLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>&#160;</div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>&#160;</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>&#160;    Pooling2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>&#160;}</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>&#160;</div><div class="line"><a name="l01950"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aba0c861593907248e1d293a588383bb1"> 1950</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeQuantize)</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>&#160;{</div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(<a class="code" href="namespacearmnn.xhtml#ad773a034fb9983e15f3094b4c5c7c30c">Quantize</a>)</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>&#160;</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;quantize&quot;</span>);</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>&#160;</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> quantizeLayer = network-&gt;AddQuantizeLayer(layerName.c_str());</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>&#160;</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>&#160;    quantizeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>&#160;</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>&#160;    quantizeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>&#160;</div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>&#160;</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>&#160;    QuantizeLayerVerifier verifier(layerName, {info}, {info});</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>&#160;}</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>&#160;</div><div class="line"><a name="l01975"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a6e0afb5f057cb45dbbcf7c5efad61f20"> 1975</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeReshape)</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>&#160;{</div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(Reshape)</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>&#160;</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;reshape&quot;</span>);</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 9}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({3, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>&#160;</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>&#160;    <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a> descriptor({3, 3});</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>&#160;</div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> reshapeLayer = network-&gt;AddReshapeLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>&#160;</div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(reshapeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>&#160;    reshapeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>&#160;</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>&#160;    reshapeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>&#160;</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>&#160;</div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>&#160;    ReshapeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>&#160;}</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>&#160;</div><div class="line"><a name="l02003"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a66859a4301a19650149508458d36d5d6"> 2003</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeResize)</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>&#160;{</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a25dc224be48103343302b5a6fd588fe7">Resize</a>)</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>&#160;</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;resize&quot;</span>);</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo  = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 5, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>&#160;</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>&#160;    <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a> desc;</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>&#160;    desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a>  = 4;</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>&#160;    desc.m_TargetHeight = 2;</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>&#160;    desc.m_Method       = <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaa020331bb30d2fa2ecf7c3a0777823f">armnn::ResizeMethod::NearestNeighbor</a>;</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>&#160;</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> resizeLayer = network-&gt;AddResizeLayer(desc, layerName.c_str());</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>&#160;</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(resizeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>&#160;    resizeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>&#160;</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>&#160;    resizeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>&#160;</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>&#160;</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>&#160;    ResizeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>&#160;}</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>&#160;</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>&#160;<span class="keyword">class </span>ResizeBilinearLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor&lt;armnn::ResizeBilinearDescriptor&gt;</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>&#160;{</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>&#160;    ResizeBilinearLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>&#160;                                <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>&#160;                                <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>&#160;                                <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_resize_bilinear_descriptor.xhtml">armnn::ResizeBilinearDescriptor</a>&amp; descriptor)</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span>&#160;        : LayerVerifierBaseWithDescriptor&lt;armnn::ResizeBilinearDescriptor&gt;(</div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>&#160;            layerName, inputInfos, outputInfos, descriptor) {}</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>&#160;</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>&#160;    <span class="keywordtype">void</span> VisitResizeLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>&#160;                          <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a>&amp; descriptor,</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>&#160;                          <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>&#160;        VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>&#160;</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>&#160;        <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">m_Method</a>       == <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::ResizeMethod::Bilinear</a>);</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>&#160;        <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a>  == m_Descriptor.m_TargetWidth);</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>&#160;        <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">m_TargetHeight</a> == m_Descriptor.m_TargetHeight);</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>&#160;        <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>   == m_Descriptor.m_DataLayout);</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>&#160;    }</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>&#160;</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>&#160;    <span class="keywordtype">void</span> VisitResizeBilinearLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*,</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>&#160; 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   <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;resizeBilinear&quot;</span>);</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo  = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 5, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>&#160;</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>&#160; 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   <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a044ea0cc993d4d1fbe4ec877b17b8d39">Slice</a>)</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>&#160;</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>&#160;    <span class="keyword">const</span> std::string layerName{<span class="stringliteral">&quot;slice&quot;</span>};</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>&#160;</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo  = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({3, 2, 3, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>&#160; 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   spaceToBatchNdLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span>&#160;</div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span>&#160;    spaceToBatchNdLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span>&#160;</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02237"></a><span class="lineno"> 2237</span>&#160;</div><div class="line"><a name="l02238"></a><span class="lineno"> 2238</span>&#160;    SpaceToBatchNdLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span>&#160;}</div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span>&#160;</div><div class="line"><a name="l02242"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a46f46381910339f4154d071b074df35e"> 2242</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeSpaceToDepth)</div><div class="line"><a name="l02243"></a><span class="lineno"> 2243</span>&#160;{</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a5e1dc69443b64ad16b669388a6023f7a">SpaceToDepth</a>)</div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>&#160;</div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;spaceToDepth&quot;</span>);</div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span>&#160;</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 16, 8,  3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1,  8, 4, 12 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span>&#160;</div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span>&#160;    <a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::SpaceToDepthDescriptor</a> desc;</div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span>&#160;    desc.<a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml#a6c6b8957f1e176867e5fb05b1a1a1486">m_BlockSize</a>  = 2;</div><div class="line"><a name="l02253"></a><span class="lineno"> 2253</span>&#160;    desc.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span>&#160;</div><div class="line"><a name="l02255"></a><span class="lineno"> 2255</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02256"></a><span class="lineno"> 2256</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer        = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> spaceToDepthLayer = network-&gt;AddSpaceToDepthLayer(desc, layerName.c_str());</div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer       = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span>&#160;</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(spaceToDepthLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02261"></a><span class="lineno"> 2261</span>&#160;    spaceToDepthLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>&#160;</div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>&#160;    spaceToDepthLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>&#160;</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span>&#160;</div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span>&#160;    SpaceToDepthLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02270"></a><span class="lineno"> 2270</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02271"></a><span class="lineno"> 2271</span>&#160;}</div><div class="line"><a name="l02272"></a><span class="lineno"> 2272</span>&#160;</div><div class="line"><a name="l02273"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aad7569c190fcdb4901e8665c80df013b"> 2273</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeSplitter)</div><div class="line"><a name="l02274"></a><span class="lineno"> 2274</span>&#160;{</div><div class="line"><a name="l02275"></a><span class="lineno"> 2275</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a427c3d26d05b518b1ace407035f5920e">Splitter</a>)</div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>&#160;</div><div class="line"><a name="l02277"></a><span class="lineno"> 2277</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numViews = 3;</div><div class="line"><a name="l02278"></a><span class="lineno"> 2278</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDimensions = 4;</div><div class="line"><a name="l02279"></a><span class="lineno"> 2279</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = {1, 18, 4, 4};</div><div class="line"><a name="l02280"></a><span class="lineno"> 2280</span>&#160; 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                                       <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(inputShape[2]),</div><div class="line"><a name="l02286"></a><span class="lineno"> 2286</span>&#160;                                        static_cast&lt;unsigned int&gt;(inputShape[3])};</div><div class="line"><a name="l02287"></a><span class="lineno"> 2287</span>&#160;    splitterDimSizes[1] /= numViews;</div><div class="line"><a name="l02288"></a><span class="lineno"> 2288</span>&#160;    <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a> desc(numViews, numDimensions);</div><div class="line"><a name="l02289"></a><span class="lineno"> 2289</span>&#160;</div><div class="line"><a name="l02290"></a><span class="lineno"> 2290</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g &lt; numViews; ++g)</div><div class="line"><a name="l02291"></a><span class="lineno"> 2291</span>&#160;    {</div><div class="line"><a name="l02292"></a><span class="lineno"> 2292</span>&#160;        desc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(g, 1, splitterDimSizes[1] * g);</div><div class="line"><a name="l02293"></a><span class="lineno"> 2293</span>&#160;</div><div class="line"><a name="l02294"></a><span class="lineno"> 2294</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx=0; dimIdx &lt; 4; dimIdx++)</div><div class="line"><a name="l02295"></a><span class="lineno"> 2295</span>&#160;        {</div><div class="line"><a name="l02296"></a><span class="lineno"> 2296</span>&#160;            desc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(g, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l02297"></a><span class="lineno"> 2297</span>&#160; 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   splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02313"></a><span class="lineno"> 2313</span>&#160;    splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02314"></a><span class="lineno"> 2314</span>&#160;    splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer2-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02315"></a><span class="lineno"> 2315</span>&#160;</div><div class="line"><a name="l02316"></a><span class="lineno"> 2316</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02317"></a><span class="lineno"> 2317</span>&#160;    splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02318"></a><span class="lineno"> 2318</span>&#160;    splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>&#160;    splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02320"></a><span class="lineno"> 2320</span>&#160;</div><div class="line"><a name="l02321"></a><span class="lineno"> 2321</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02323"></a><span class="lineno"> 2323</span>&#160;</div><div class="line"><a name="l02324"></a><span class="lineno"> 2324</span>&#160;    SplitterLayerVerifier verifier(layerName, {inputInfo}, {outputInfo, outputInfo, outputInfo}, desc);</div><div class="line"><a name="l02325"></a><span class="lineno"> 2325</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02326"></a><span class="lineno"> 2326</span>&#160;}</div><div class="line"><a name="l02327"></a><span class="lineno"> 2327</span>&#160;</div><div class="line"><a name="l02328"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a9e3547d945fb7ee85e09cfd3423780a9"> 2328</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeStack)</div><div class="line"><a name="l02329"></a><span class="lineno"> 2329</span>&#160;{</div><div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a6ef2dcac2ec0683d52df1b051404e7d6">Stack</a>)</div><div class="line"><a name="l02331"></a><span class="lineno"> 2331</span>&#160;</div><div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;stack&quot;</span>);</div><div class="line"><a name="l02333"></a><span class="lineno"> 2333</span>&#160;</div><div class="line"><a name="l02334"></a><span class="lineno"> 2334</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo ({4, 3, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({4, 3, 2, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02336"></a><span class="lineno"> 2336</span>&#160;</div><div class="line"><a name="l02337"></a><span class="lineno"> 2337</span>&#160;    <a class="code" href="structarmnn_1_1_stack_descriptor.xhtml">armnn::StackDescriptor</a> descriptor(2, 2, {4, 3, 5});</div><div class="line"><a name="l02338"></a><span class="lineno"> 2338</span>&#160;</div><div class="line"><a name="l02339"></a><span class="lineno"> 2339</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02340"></a><span class="lineno"> 2340</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l02341"></a><span class="lineno"> 2341</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer2 = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l02342"></a><span class="lineno"> 2342</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> stackLayer = network-&gt;AddStackLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02343"></a><span class="lineno"> 2343</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l02344"></a><span class="lineno"> 2344</span>&#160;</div><div class="line"><a name="l02345"></a><span class="lineno"> 2345</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(stackLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02346"></a><span class="lineno"> 2346</span>&#160;    inputLayer2-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(stackLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02347"></a><span class="lineno"> 2347</span>&#160;    stackLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02348"></a><span class="lineno"> 2348</span>&#160;</div><div class="line"><a name="l02349"></a><span class="lineno"> 2349</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l02350"></a><span class="lineno"> 2350</span>&#160;    inputLayer2-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l02351"></a><span class="lineno"> 2351</span>&#160;    stackLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02352"></a><span class="lineno"> 2352</span>&#160;</div><div class="line"><a name="l02353"></a><span class="lineno"> 2353</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02354"></a><span class="lineno"> 2354</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02355"></a><span class="lineno"> 2355</span>&#160;</div><div class="line"><a name="l02356"></a><span class="lineno"> 2356</span>&#160;    StackLayerVerifier verifier(layerName, {inputTensorInfo, inputTensorInfo}, {outputTensorInfo}, descriptor);</div><div class="line"><a name="l02357"></a><span class="lineno"> 2357</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02358"></a><span class="lineno"> 2358</span>&#160;}</div><div class="line"><a name="l02359"></a><span class="lineno"> 2359</span>&#160;</div><div class="line"><a name="l02360"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a865de239f4cf854e65f8b61ebbbb7fbd"> 2360</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeStandIn)</div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span>&#160;{</div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(StandIn)</div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span>&#160;</div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;standIn&quot;</span>);</div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span>&#160;</div><div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({ 1u }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02367"></a><span class="lineno"> 2367</span>&#160;    <a class="code" href="structarmnn_1_1_stand_in_descriptor.xhtml">armnn::StandInDescriptor</a> descriptor(2u, 2u);</div><div class="line"><a name="l02368"></a><span class="lineno"> 2368</span>&#160;</div><div class="line"><a name="l02369"></a><span class="lineno"> 2369</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02370"></a><span class="lineno"> 2370</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0  = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1  = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l02372"></a><span class="lineno"> 2372</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> standInLayer = network-&gt;AddStandInLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02373"></a><span class="lineno"> 2373</span>&#160; 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   inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l02378"></a><span class="lineno"> 2378</span>&#160;</div><div class="line"><a name="l02379"></a><span class="lineno"> 2379</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(standInLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02380"></a><span class="lineno"> 2380</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l02381"></a><span class="lineno"> 2381</span>&#160;</div><div class="line"><a name="l02382"></a><span class="lineno"> 2382</span>&#160;    standInLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span>&#160;    standInLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l02384"></a><span class="lineno"> 2384</span>&#160;</div><div class="line"><a name="l02385"></a><span class="lineno"> 2385</span>&#160;    standInLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span>&#160;    standInLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l02387"></a><span class="lineno"> 2387</span>&#160;</div><div class="line"><a name="l02388"></a><span class="lineno"> 2388</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span>&#160;</div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>&#160;    StandInLayerVerifier verifier(layerName, { tensorInfo, tensorInfo }, { tensorInfo, tensorInfo }, descriptor);</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>&#160;}</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span>&#160;</div><div class="line"><a name="l02395"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a2db8caccb8225bbef2368872aa9355d1"> 2395</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeStridedSlice)</div><div class="line"><a name="l02396"></a><span class="lineno"> 2396</span>&#160;{</div><div class="line"><a name="l02397"></a><span class="lineno"> 2397</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a86d7a7168ac00b75b4971f9aad623698">StridedSlice</a>)</div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span>&#160;</div><div class="line"><a name="l02399"></a><span class="lineno"> 2399</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;stridedSlice&quot;</span>);</div><div class="line"><a name="l02400"></a><span class="lineno"> 2400</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({3, 2, 3, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02401"></a><span class="lineno"> 2401</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({3, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span>&#160;</div><div class="line"><a name="l02403"></a><span class="lineno"> 2403</span>&#160;    <a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml">armnn::StridedSliceDescriptor</a> desc({0, 0, 1, 0}, {1, 1, 1, 1}, {1, 1, 1, 1});</div><div class="line"><a name="l02404"></a><span class="lineno"> 2404</span>&#160;    desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">m_EndMask</a> = (1 &lt;&lt; 4) - 1;</div><div class="line"><a name="l02405"></a><span class="lineno"> 2405</span>&#160;    desc.m_ShrinkAxisMask = (1 &lt;&lt; 1) | (1 &lt;&lt; 2);</div><div class="line"><a name="l02406"></a><span class="lineno"> 2406</span>&#160;    desc.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l02407"></a><span class="lineno"> 2407</span>&#160;</div><div class="line"><a name="l02408"></a><span class="lineno"> 2408</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02409"></a><span class="lineno"> 2409</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l02410"></a><span class="lineno"> 2410</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> stridedSliceLayer = network-&gt;AddStridedSliceLayer(desc, layerName.c_str());</div><div class="line"><a name="l02411"></a><span class="lineno"> 2411</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l02412"></a><span class="lineno"> 2412</span>&#160;</div><div class="line"><a name="l02413"></a><span class="lineno"> 2413</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(stridedSliceLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02414"></a><span class="lineno"> 2414</span>&#160;    stridedSliceLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02415"></a><span class="lineno"> 2415</span>&#160;</div><div class="line"><a name="l02416"></a><span class="lineno"> 2416</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02417"></a><span class="lineno"> 2417</span>&#160;    stridedSliceLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02418"></a><span class="lineno"> 2418</span>&#160;</div><div class="line"><a name="l02419"></a><span class="lineno"> 2419</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02420"></a><span class="lineno"> 2420</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02421"></a><span class="lineno"> 2421</span>&#160;</div><div class="line"><a name="l02422"></a><span class="lineno"> 2422</span>&#160;    StridedSliceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02423"></a><span class="lineno"> 2423</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</span>&#160;}</div><div class="line"><a name="l02425"></a><span class="lineno"> 2425</span>&#160;</div><div class="line"><a name="l02426"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a1ceee00de3ecf9d68bdebed498cd049a"> 2426</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeSubtraction)</div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</span>&#160;{</div><div class="line"><a name="l02428"></a><span class="lineno"> 2428</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Subtraction)</div><div class="line"><a name="l02429"></a><span class="lineno"> 2429</span>&#160;</div><div class="line"><a name="l02430"></a><span class="lineno"> 2430</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;subtraction&quot;</span>);</div><div class="line"><a name="l02431"></a><span class="lineno"> 2431</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02432"></a><span class="lineno"> 2432</span>&#160;</div><div class="line"><a name="l02433"></a><span class="lineno"> 2433</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02434"></a><span class="lineno"> 2434</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l02435"></a><span class="lineno"> 2435</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l02436"></a><span class="lineno"> 2436</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> subtractionLayer = network-&gt;AddSubtractionLayer(layerName.c_str());</div><div class="line"><a name="l02437"></a><span class="lineno"> 2437</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l02438"></a><span class="lineno"> 2438</span>&#160;</div><div class="line"><a name="l02439"></a><span class="lineno"> 2439</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(subtractionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(subtractionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02441"></a><span class="lineno"> 2441</span>&#160;    subtractionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02442"></a><span class="lineno"> 2442</span>&#160;</div><div class="line"><a name="l02443"></a><span class="lineno"> 2443</span>&#160;    inputLayer0-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02444"></a><span class="lineno"> 2444</span>&#160;    inputLayer1-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02445"></a><span class="lineno"> 2445</span>&#160;    subtractionLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02446"></a><span class="lineno"> 2446</span>&#160;</div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span>&#160;</div><div class="line"><a name="l02450"></a><span class="lineno"> 2450</span>&#160;    SubtractionLayerVerifier verifier(layerName, {info, info}, {info});</div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02452"></a><span class="lineno"> 2452</span>&#160;}</div><div class="line"><a name="l02453"></a><span class="lineno"> 2453</span>&#160;</div><div class="line"><a name="l02454"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a2ae604a52ed7b47ffe458eaff5675476"> 2454</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeSwitch)</div><div class="line"><a name="l02455"></a><span class="lineno"> 2455</span>&#160;{</div><div class="line"><a name="l02456"></a><span class="lineno"> 2456</span>&#160;    <span class="keyword">class </span>SwitchLayerVerifier : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l02457"></a><span class="lineno"> 2457</span>&#160;    {</div><div class="line"><a name="l02458"></a><span class="lineno"> 2458</span>&#160;    <span class="keyword">public</span>:</div><div class="line"><a name="l02459"></a><span class="lineno"> 2459</span>&#160;        SwitchLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l02460"></a><span class="lineno"> 2460</span>&#160;                            <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l02461"></a><span class="lineno"> 2461</span>&#160;                            <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos)</div><div class="line"><a name="l02462"></a><span class="lineno"> 2462</span>&#160;            : LayerVerifierBase(layerName, inputInfos, outputInfos) {}</div><div class="line"><a name="l02463"></a><span class="lineno"> 2463</span>&#160;</div><div class="line"><a name="l02464"></a><span class="lineno"> 2464</span>&#160;        <span class="keywordtype">void</span> VisitSwitchLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer, <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l02465"></a><span class="lineno"> 2465</span>&#160;<span class="keyword">        </span>{</div><div class="line"><a name="l02466"></a><span class="lineno"> 2466</span>&#160;            VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02467"></a><span class="lineno"> 2467</span>&#160;        }</div><div class="line"><a name="l02468"></a><span class="lineno"> 2468</span>&#160;</div><div class="line"><a name="l02469"></a><span class="lineno"> 2469</span>&#160;        <span class="keywordtype">void</span> VisitConstantLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*,</div><div class="line"><a name="l02470"></a><span class="lineno"> 2470</span>&#160;                                <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp;,</div><div class="line"><a name="l02471"></a><span class="lineno"> 2471</span>&#160;                                <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override </span>{}</div><div class="line"><a name="l02472"></a><span class="lineno"> 2472</span>&#160;    };</div><div class="line"><a name="l02473"></a><span class="lineno"> 2473</span>&#160;</div><div class="line"><a name="l02474"></a><span class="lineno"> 2474</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;switch&quot;</span>);</div><div class="line"><a name="l02475"></a><span class="lineno"> 2475</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02476"></a><span class="lineno"> 2476</span>&#160;</div><div class="line"><a name="l02477"></a><span class="lineno"> 2477</span>&#160;    std::vector&lt;float&gt; constantData = GenerateRandomData&lt;float&gt;(info.GetNumElements());</div><div class="line"><a name="l02478"></a><span class="lineno"> 2478</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> constTensor(info, constantData);</div><div class="line"><a name="l02479"></a><span class="lineno"> 2479</span>&#160;</div><div class="line"><a name="l02480"></a><span class="lineno"> 2480</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02481"></a><span class="lineno"> 2481</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> constantLayer = network-&gt;AddConstantLayer(constTensor, <span class="stringliteral">&quot;constant&quot;</span>);</div><div class="line"><a name="l02483"></a><span class="lineno"> 2483</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> switchLayer = network-&gt;AddSwitchLayer(layerName.c_str());</div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> trueOutputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l02485"></a><span class="lineno"> 2485</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> falseOutputLayer = network-&gt;AddOutputLayer(1);</div><div class="line"><a name="l02486"></a><span class="lineno"> 2486</span>&#160;</div><div class="line"><a name="l02487"></a><span class="lineno"> 2487</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(switchLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02488"></a><span class="lineno"> 2488</span>&#160;    constantLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(switchLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02489"></a><span class="lineno"> 2489</span>&#160;    switchLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(trueOutputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02490"></a><span class="lineno"> 2490</span>&#160;    switchLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(falseOutputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02491"></a><span class="lineno"> 2491</span>&#160;</div><div class="line"><a name="l02492"></a><span class="lineno"> 2492</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02493"></a><span class="lineno"> 2493</span>&#160;    constantLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02494"></a><span class="lineno"> 2494</span>&#160;    switchLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>&#160;    switchLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02496"></a><span class="lineno"> 2496</span>&#160;</div><div class="line"><a name="l02497"></a><span class="lineno"> 2497</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02498"></a><span class="lineno"> 2498</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02499"></a><span class="lineno"> 2499</span>&#160;</div><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>&#160;    SwitchLayerVerifier verifier(layerName, {info, info}, {info, info});</div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span>&#160;}</div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span>&#160;</div><div class="line"><a name="l02504"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a11f5fe3da18636059c4d8c21e11ac3f5"> 2504</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeTranspose)</div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</span>&#160;{</div><div class="line"><a name="l02506"></a><span class="lineno"> 2506</span>&#160;    <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn_utils.xhtml#a405d5f966ec992d1717711e5a2d7909d">Transpose</a>)</div><div class="line"><a name="l02507"></a><span class="lineno"> 2507</span>&#160;</div><div class="line"><a name="l02508"></a><span class="lineno"> 2508</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;transpose&quot;</span>);</div><div class="line"><a name="l02509"></a><span class="lineno"> 2509</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({4, 3, 2, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02510"></a><span class="lineno"> 2510</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({1, 2, 3, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02511"></a><span class="lineno"> 2511</span>&#160;</div><div class="line"><a name="l02512"></a><span class="lineno"> 2512</span>&#160;    <a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">armnn::TransposeDescriptor</a> descriptor(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>({3, 2, 1, 0}));</div><div class="line"><a name="l02513"></a><span class="lineno"> 2513</span>&#160;</div><div class="line"><a name="l02514"></a><span class="lineno"> 2514</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02515"></a><span class="lineno"> 2515</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l02516"></a><span class="lineno"> 2516</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> transposeLayer = network-&gt;AddTransposeLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span>&#160;</div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(transposeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02520"></a><span class="lineno"> 2520</span>&#160;    transposeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02521"></a><span class="lineno"> 2521</span>&#160;</div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>&#160;    transposeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02524"></a><span class="lineno"> 2524</span>&#160;</div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02527"></a><span class="lineno"> 2527</span>&#160;</div><div class="line"><a name="l02528"></a><span class="lineno"> 2528</span>&#160;    TransposeLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);</div><div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02530"></a><span class="lineno"> 2530</span>&#160;}</div><div class="line"><a name="l02531"></a><span class="lineno"> 2531</span>&#160;</div><div class="line"><a name="l02532"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#acb977ca4fb62419f89117fa33c5a4d4a"> 2532</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeTransposeConvolution2d)</div><div class="line"><a name="l02533"></a><span class="lineno"> 2533</span>&#160;{</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span>&#160;    <span class="keyword">using</span> Descriptor = <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a>;</div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>&#160;    <span class="keyword">class </span>TransposeConvolution2dLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor&lt;Descriptor&gt;</div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span>&#160;    {</div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span>&#160;    <span class="keyword">public</span>:</div><div class="line"><a name="l02538"></a><span class="lineno"> 2538</span>&#160;        TransposeConvolution2dLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l02539"></a><span class="lineno"> 2539</span>&#160;                                            <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l02540"></a><span class="lineno"> 2540</span>&#160;                                            <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l02541"></a><span class="lineno"> 2541</span>&#160;                                            <span class="keyword">const</span> Descriptor&amp; descriptor,</div><div class="line"><a name="l02542"></a><span class="lineno"> 2542</span>&#160;                                            <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; weights,</div><div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>&#160;                                            <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>&amp; biases)</div><div class="line"><a name="l02544"></a><span class="lineno"> 2544</span>&#160;            : LayerVerifierBaseWithDescriptor&lt;Descriptor&gt;(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l02545"></a><span class="lineno"> 2545</span>&#160;            , m_Weights(weights)</div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>&#160;            , m_Biases(biases)</div><div class="line"><a name="l02547"></a><span class="lineno"> 2547</span>&#160;        {}</div><div class="line"><a name="l02548"></a><span class="lineno"> 2548</span>&#160;</div><div class="line"><a name="l02549"></a><span class="lineno"> 2549</span>&#160;        <span class="keywordtype">void</span> VisitTransposeConvolution2dLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l02550"></a><span class="lineno"> 2550</span>&#160;                                              <span class="keyword">const</span> Descriptor&amp; descriptor,</div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span>&#160;                                              <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; weights,</div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>&#160;                                              <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>&amp; biases,</div><div class="line"><a name="l02553"></a><span class="lineno"> 2553</span>&#160;                                              <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l02554"></a><span class="lineno"> 2554</span>&#160;<span class="keyword">        </span>{</div><div class="line"><a name="l02555"></a><span class="lineno"> 2555</span>&#160;            VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02556"></a><span class="lineno"> 2556</span>&#160;            VerifyDescriptor(descriptor);</div><div class="line"><a name="l02557"></a><span class="lineno"> 2557</span>&#160;</div><div class="line"><a name="l02558"></a><span class="lineno"> 2558</span>&#160;            <span class="comment">// check weights</span></div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span>&#160;            CompareConstTensor(weights, m_Weights);</div><div class="line"><a name="l02560"></a><span class="lineno"> 2560</span>&#160;</div><div class="line"><a name="l02561"></a><span class="lineno"> 2561</span>&#160;            <span class="comment">// check biases</span></div><div class="line"><a name="l02562"></a><span class="lineno"> 2562</span>&#160;            <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == descriptor.m_BiasEnabled);</div><div class="line"><a name="l02563"></a><span class="lineno"> 2563</span>&#160;            <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == m_Biases.has_value());</div><div class="line"><a name="l02564"></a><span class="lineno"> 2564</span>&#160;</div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span>&#160;            <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() &amp;&amp; m_Biases.has_value())</div><div class="line"><a name="l02566"></a><span class="lineno"> 2566</span>&#160;            {</div><div class="line"><a name="l02567"></a><span class="lineno"> 2567</span>&#160;                CompareConstTensor(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(), m_Biases.value());</div><div class="line"><a name="l02568"></a><span class="lineno"> 2568</span>&#160;            }</div><div class="line"><a name="l02569"></a><span class="lineno"> 2569</span>&#160;        }</div><div class="line"><a name="l02570"></a><span class="lineno"> 2570</span>&#160;</div><div class="line"><a name="l02571"></a><span class="lineno"> 2571</span>&#160;    <span class="keyword">private</span>:</div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>                      m_Weights;</div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span>&#160;        <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>     m_Biases;</div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>&#160;    };</div><div class="line"><a name="l02575"></a><span class="lineno"> 2575</span>&#160;</div><div class="line"><a name="l02576"></a><span class="lineno"> 2576</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;transposeConvolution2d&quot;</span>);</div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 7, 7, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02578"></a><span class="lineno"> 2578</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 9, 9, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02579"></a><span class="lineno"> 2579</span>&#160;</div><div class="line"><a name="l02580"></a><span class="lineno"> 2580</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02581"></a><span class="lineno"> 2581</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02582"></a><span class="lineno"> 2582</span>&#160;</div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span>&#160;    std::vector&lt;float&gt; weightsData = GenerateRandomData&lt;float&gt;(weightsInfo.GetNumElements());</div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span>&#160;</div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>&#160;    std::vector&lt;float&gt; biasesData = GenerateRandomData&lt;float&gt;(biasesInfo.GetNumElements());</div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</span>&#160;</div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>&#160;    <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>     = 1;</div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>    = 1;</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>      = 1;</div><div class="line"><a name="l02593"></a><span class="lineno"> 2593</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>   = 1;</div><div class="line"><a name="l02594"></a><span class="lineno"> 2594</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>     = 1;</div><div class="line"><a name="l02595"></a><span class="lineno"> 2595</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>     = 1;</div><div class="line"><a name="l02596"></a><span class="lineno"> 2596</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l02597"></a><span class="lineno"> 2597</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>  = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02598"></a><span class="lineno"> 2598</span>&#160;</div><div class="line"><a name="l02599"></a><span class="lineno"> 2599</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02600"></a><span class="lineno"> 2600</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer  = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> convLayer   =</div><div class="line"><a name="l02602"></a><span class="lineno"> 2602</span>&#160;            network-&gt;AddTransposeConvolution2dLayer(descriptor,</div><div class="line"><a name="l02603"></a><span class="lineno"> 2603</span>&#160;                                                    weights,</div><div class="line"><a name="l02604"></a><span class="lineno"> 2604</span>&#160;                                                    <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::ConstTensor&gt;</a>(biases),</div><div class="line"><a name="l02605"></a><span class="lineno"> 2605</span>&#160;                                                    layerName.c_str());</div><div class="line"><a name="l02606"></a><span class="lineno"> 2606</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l02607"></a><span class="lineno"> 2607</span>&#160;</div><div class="line"><a name="l02608"></a><span class="lineno"> 2608</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02609"></a><span class="lineno"> 2609</span>&#160;    convLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02610"></a><span class="lineno"> 2610</span>&#160;</div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02612"></a><span class="lineno"> 2612</span>&#160;    convLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02613"></a><span class="lineno"> 2613</span>&#160;</div><div class="line"><a name="l02614"></a><span class="lineno"> 2614</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02616"></a><span class="lineno"> 2616</span>&#160;</div><div class="line"><a name="l02617"></a><span class="lineno"> 2617</span>&#160;    TransposeConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);</div><div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02619"></a><span class="lineno"> 2619</span>&#160;}</div><div class="line"><a name="l02620"></a><span class="lineno"> 2620</span>&#160;</div><div class="line"><a name="l02621"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aca4adebc92f5a0afc5969f5be06ec2b4"> 2621</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeNonLinearNetwork)</div><div class="line"><a name="l02622"></a><span class="lineno"> 2622</span>&#160;{</div><div class="line"><a name="l02623"></a><span class="lineno"> 2623</span>&#160;    <span class="keyword">class </span>ConstantLayerVerifier : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l02624"></a><span class="lineno"> 2624</span>&#160;    {</div><div class="line"><a name="l02625"></a><span class="lineno"> 2625</span>&#160;    <span class="keyword">public</span>:</div><div class="line"><a name="l02626"></a><span class="lineno"> 2626</span>&#160;        ConstantLayerVerifier(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l02627"></a><span class="lineno"> 2627</span>&#160;                              <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l02628"></a><span class="lineno"> 2628</span>&#160;                              <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l02629"></a><span class="lineno"> 2629</span>&#160;                              <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; layerInput)</div><div class="line"><a name="l02630"></a><span class="lineno"> 2630</span>&#160;            : LayerVerifierBase(layerName, inputInfos, outputInfos)</div><div class="line"><a name="l02631"></a><span class="lineno"> 2631</span>&#160;            , m_LayerInput(layerInput) {}</div><div class="line"><a name="l02632"></a><span class="lineno"> 2632</span>&#160;</div><div class="line"><a name="l02633"></a><span class="lineno"> 2633</span>&#160;        <span class="keywordtype">void</span> VisitConstantLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l02634"></a><span class="lineno"> 2634</span>&#160;                                <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&amp; input,</div><div class="line"><a name="l02635"></a><span class="lineno"> 2635</span>&#160;                                <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l02636"></a><span class="lineno"> 2636</span>&#160;<span class="keyword">        </span>{</div><div class="line"><a name="l02637"></a><span class="lineno"> 2637</span>&#160;            VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02638"></a><span class="lineno"> 2638</span>&#160;            CompareConstTensor(input, m_LayerInput);</div><div class="line"><a name="l02639"></a><span class="lineno"> 2639</span>&#160;        }</div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span>&#160;</div><div class="line"><a name="l02641"></a><span class="lineno"> 2641</span>&#160;        <span class="keywordtype">void</span> VisitAdditionLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override </span>{}</div><div class="line"><a name="l02642"></a><span class="lineno"> 2642</span>&#160;</div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span>&#160;    <span class="keyword">private</span>:</div><div class="line"><a name="l02644"></a><span class="lineno"> 2644</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_LayerInput;</div><div class="line"><a name="l02645"></a><span class="lineno"> 2645</span>&#160;    };</div><div class="line"><a name="l02646"></a><span class="lineno"> 2646</span>&#160;</div><div class="line"><a name="l02647"></a><span class="lineno"> 2647</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;constant&quot;</span>);</div><div class="line"><a name="l02648"></a><span class="lineno"> 2648</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02649"></a><span class="lineno"> 2649</span>&#160;</div><div class="line"><a name="l02650"></a><span class="lineno"> 2650</span>&#160;    std::vector&lt;float&gt; constantData = GenerateRandomData&lt;float&gt;(info.GetNumElements());</div><div class="line"><a name="l02651"></a><span class="lineno"> 2651</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> constTensor(info, constantData);</div><div class="line"><a name="l02652"></a><span class="lineno"> 2652</span>&#160;</div><div class="line"><a name="l02653"></a><span class="lineno"> 2653</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network(<a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>());</div><div class="line"><a name="l02654"></a><span class="lineno"> 2654</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* input = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l02655"></a><span class="lineno"> 2655</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* add = network-&gt;AddAdditionLayer();</div><div class="line"><a name="l02656"></a><span class="lineno"> 2656</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* constant = network-&gt;AddConstantLayer(constTensor, layerName.c_str());</div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* output = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</span>&#160;</div><div class="line"><a name="l02659"></a><span class="lineno"> 2659</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(add-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>&#160;    constant-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(add-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span>&#160;    add-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span>&#160;</div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02664"></a><span class="lineno"> 2664</span>&#160;    constant-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02665"></a><span class="lineno"> 2665</span>&#160;    add-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02666"></a><span class="lineno"> 2666</span>&#160;</div><div class="line"><a name="l02667"></a><span class="lineno"> 2667</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02668"></a><span class="lineno"> 2668</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02669"></a><span class="lineno"> 2669</span>&#160;</div><div class="line"><a name="l02670"></a><span class="lineno"> 2670</span>&#160;    ConstantLayerVerifier verifier(layerName, {}, {info}, constTensor);</div><div class="line"><a name="l02671"></a><span class="lineno"> 2671</span>&#160;    deserializedNetwork-&gt;Accept(verifier);</div><div class="line"><a name="l02672"></a><span class="lineno"> 2672</span>&#160;}</div><div class="line"><a name="l02673"></a><span class="lineno"> 2673</span>&#160;</div><div class="line"><a name="l02674"></a><span class="lineno"> 2674</span>&#160;<span class="keyword">class </span>VerifyLstmLayer : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor&lt;armnn::LstmDescriptor&gt;</div><div class="line"><a name="l02675"></a><span class="lineno"> 2675</span>&#160;{</div><div class="line"><a name="l02676"></a><span class="lineno"> 2676</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>&#160;    VerifyLstmLayer(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>&#160;                    <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l02679"></a><span class="lineno"> 2679</span>&#160;                    <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l02680"></a><span class="lineno"> 2680</span>&#160;                    <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a>&amp; descriptor,</div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>&#160;                    <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a>&amp; inputParams)</div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</span>&#160;        : LayerVerifierBaseWithDescriptor&lt;armnn::LstmDescriptor&gt;(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l02683"></a><span class="lineno"> 2683</span>&#160;        , m_InputParams(inputParams) {}</div><div class="line"><a name="l02684"></a><span class="lineno"> 2684</span>&#160;</div><div class="line"><a name="l02685"></a><span class="lineno"> 2685</span>&#160;    <span class="keywordtype">void</span> VisitLstmLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l02686"></a><span class="lineno"> 2686</span>&#160;                        <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a>&amp; descriptor,</div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</span>&#160;                        <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a>&amp; params,</div><div class="line"><a name="l02688"></a><span class="lineno"> 2688</span>&#160;                        <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l02689"></a><span class="lineno"> 2689</span>&#160;    {</div><div class="line"><a name="l02690"></a><span class="lineno"> 2690</span>&#160;        VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02691"></a><span class="lineno"> 2691</span>&#160;        VerifyDescriptor(descriptor);</div><div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>&#160;        VerifyInputParameters(params);</div><div class="line"><a name="l02693"></a><span class="lineno"> 2693</span>&#160;    }</div><div class="line"><a name="l02694"></a><span class="lineno"> 2694</span>&#160;</div><div class="line"><a name="l02695"></a><span class="lineno"> 2695</span>&#160;<span class="keyword">protected</span>:</div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>&#160;    <span class="keywordtype">void</span> VerifyInputParameters(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a>&amp; params)</div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>&#160;    {</div><div class="line"><a name="l02698"></a><span class="lineno"> 2698</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02699"></a><span class="lineno"> 2699</span>&#160;            <span class="stringliteral">&quot;m_InputToInputWeights&quot;</span>, m_InputParams.m_InputToInputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a>);</div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span>&#160;            <span class="stringliteral">&quot;m_InputToForgetWeights&quot;</span>, m_InputParams.m_InputToForgetWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a>);</div><div class="line"><a name="l02702"></a><span class="lineno"> 2702</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02703"></a><span class="lineno"> 2703</span>&#160;            <span class="stringliteral">&quot;m_InputToCellWeights&quot;</span>, m_InputParams.m_InputToCellWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a>);</div><div class="line"><a name="l02704"></a><span class="lineno"> 2704</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02705"></a><span class="lineno"> 2705</span>&#160;            <span class="stringliteral">&quot;m_InputToOutputWeights&quot;</span>, m_InputParams.m_InputToOutputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a>);</div><div class="line"><a name="l02706"></a><span class="lineno"> 2706</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>&#160;            <span class="stringliteral">&quot;m_RecurrentToInputWeights&quot;</span>, m_InputParams.m_RecurrentToInputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a>);</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02709"></a><span class="lineno"> 2709</span>&#160;            <span class="stringliteral">&quot;m_RecurrentToForgetWeights&quot;</span>, m_InputParams.m_RecurrentToForgetWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a>);</div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span>&#160;            <span class="stringliteral">&quot;m_RecurrentToCellWeights&quot;</span>, m_InputParams.m_RecurrentToCellWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>);</div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</span>&#160;            <span class="stringliteral">&quot;m_RecurrentToOutputWeights&quot;</span>, m_InputParams.m_RecurrentToOutputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a>);</div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02715"></a><span class="lineno"> 2715</span>&#160;            <span class="stringliteral">&quot;m_CellToInputWeights&quot;</span>, m_InputParams.m_CellToInputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a>);</div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</span>&#160;            <span class="stringliteral">&quot;m_CellToForgetWeights&quot;</span>, m_InputParams.m_CellToForgetWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a>);</div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</span>&#160;            <span class="stringliteral">&quot;m_CellToOutputWeights&quot;</span>, m_InputParams.m_CellToOutputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a>);</div><div class="line"><a name="l02720"></a><span class="lineno"> 2720</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span>&#160;            <span class="stringliteral">&quot;m_InputGateBias&quot;</span>, m_InputParams.m_InputGateBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a>);</div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02723"></a><span class="lineno"> 2723</span>&#160;            <span class="stringliteral">&quot;m_ForgetGateBias&quot;</span>, m_InputParams.m_ForgetGateBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a>);</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02725"></a><span class="lineno"> 2725</span>&#160;            <span class="stringliteral">&quot;m_CellBias&quot;</span>, m_InputParams.m_CellBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a>);</div><div class="line"><a name="l02726"></a><span class="lineno"> 2726</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span>&#160;            <span class="stringliteral">&quot;m_OutputGateBias&quot;</span>, m_InputParams.m_OutputGateBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a>);</div><div class="line"><a name="l02728"></a><span class="lineno"> 2728</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02729"></a><span class="lineno"> 2729</span>&#160;            <span class="stringliteral">&quot;m_ProjectionWeights&quot;</span>, m_InputParams.m_ProjectionWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a>);</div><div class="line"><a name="l02730"></a><span class="lineno"> 2730</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02731"></a><span class="lineno"> 2731</span>&#160;            <span class="stringliteral">&quot;m_ProjectionBias&quot;</span>, m_InputParams.m_ProjectionBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a>);</div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span>&#160;            <span class="stringliteral">&quot;m_InputLayerNormWeights&quot;</span>, m_InputParams.m_InputLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a>);</div><div class="line"><a name="l02734"></a><span class="lineno"> 2734</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span>&#160;            <span class="stringliteral">&quot;m_ForgetLayerNormWeights&quot;</span>, m_InputParams.m_ForgetLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a>);</div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02737"></a><span class="lineno"> 2737</span>&#160;            <span class="stringliteral">&quot;m_CellLayerNormWeights&quot;</span>, m_InputParams.m_CellLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a>);</div><div class="line"><a name="l02738"></a><span class="lineno"> 2738</span>&#160;        VerifyConstTensors(</div><div class="line"><a name="l02739"></a><span class="lineno"> 2739</span>&#160;            <span class="stringliteral">&quot;m_OutputLayerNormWeights&quot;</span>, m_InputParams.m_OutputLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a>);</div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span>&#160;    }</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span>&#160;</div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l02743"></a><span class="lineno"> 2743</span>&#160;    <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> m_InputParams;</div><div class="line"><a name="l02744"></a><span class="lineno"> 2744</span>&#160;};</div><div class="line"><a name="l02745"></a><span class="lineno"> 2745</span>&#160;</div><div class="line"><a name="l02746"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960"> 2746</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeLstmCifgPeepholeNoProjection)</div><div class="line"><a name="l02747"></a><span class="lineno"> 2747</span>&#160;{</div><div class="line"><a name="l02748"></a><span class="lineno"> 2748</span>&#160;    <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a> descriptor;</div><div class="line"><a name="l02749"></a><span class="lineno"> 2749</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.0f;</div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.0f;</div><div class="line"><a name="l02752"></a><span class="lineno"> 2752</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">true</span>; <span class="comment">// if this is true then we DON&#39;T need to set the OptCifgParams</span></div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l02754"></a><span class="lineno"> 2754</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span>&#160;</div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>&#160;    <span class="keyword">const</span> uint32_t batchSize = 1;</div><div class="line"><a name="l02757"></a><span class="lineno"> 2757</span>&#160;    <span class="keyword">const</span> uint32_t inputSize = 2;</div><div class="line"><a name="l02758"></a><span class="lineno"> 2758</span>&#160; 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   lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02840"></a><span class="lineno"> 2840</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l02841"></a><span class="lineno"> 2841</span>&#160;</div><div class="line"><a name="l02842"></a><span class="lineno"> 2842</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02843"></a><span class="lineno"> 2843</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l02844"></a><span class="lineno"> 2844</span>&#160;</div><div class="line"><a name="l02845"></a><span class="lineno"> 2845</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02846"></a><span class="lineno"> 2846</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l02847"></a><span class="lineno"> 2847</span>&#160;</div><div class="line"><a name="l02848"></a><span class="lineno"> 2848</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02849"></a><span class="lineno"> 2849</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02850"></a><span class="lineno"> 2850</span>&#160;</div><div class="line"><a name="l02851"></a><span class="lineno"> 2851</span>&#160;    VerifyLstmLayer checker(</div><div class="line"><a name="l02852"></a><span class="lineno"> 2852</span>&#160;        layerName,</div><div class="line"><a name="l02853"></a><span class="lineno"> 2853</span>&#160;        {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l02854"></a><span class="lineno"> 2854</span>&#160;        {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l02855"></a><span class="lineno"> 2855</span>&#160;        descriptor,</div><div class="line"><a name="l02856"></a><span class="lineno"> 2856</span>&#160;        params);</div><div class="line"><a name="l02857"></a><span class="lineno"> 2857</span>&#160;    deserializedNetwork-&gt;Accept(checker);</div><div class="line"><a name="l02858"></a><span class="lineno"> 2858</span>&#160;}</div><div class="line"><a name="l02859"></a><span class="lineno"> 2859</span>&#160;</div><div class="line"><a name="l02860"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a884784964d5b3e12dd1a0b76e63a85f9"> 2860</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeLstmNoCifgWithPeepholeAndProjection)</div><div class="line"><a name="l02861"></a><span class="lineno"> 2861</span>&#160;{</div><div class="line"><a name="l02862"></a><span class="lineno"> 2862</span>&#160;    <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a> descriptor;</div><div class="line"><a name="l02863"></a><span class="lineno"> 2863</span>&#160; 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   <span class="keyword">const</span> uint32_t outputSize = 16;</div><div class="line"><a name="l02874"></a><span class="lineno"> 2874</span>&#160;</div><div class="line"><a name="l02875"></a><span class="lineno"> 2875</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x5({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02876"></a><span class="lineno"> 2876</span>&#160;    std::vector&lt;float&gt; inputToInputWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l02877"></a><span class="lineno"> 2877</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);</div><div class="line"><a name="l02878"></a><span class="lineno"> 2878</span>&#160;</div><div class="line"><a name="l02879"></a><span class="lineno"> 2879</span>&#160;    std::vector&lt;float&gt; inputToForgetWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l02880"></a><span class="lineno"> 2880</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);</div><div class="line"><a name="l02881"></a><span class="lineno"> 2881</span>&#160;</div><div class="line"><a name="l02882"></a><span class="lineno"> 2882</span>&#160;    std::vector&lt;float&gt; inputToCellWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l02883"></a><span class="lineno"> 2883</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);</div><div class="line"><a name="l02884"></a><span class="lineno"> 2884</span>&#160;</div><div class="line"><a name="l02885"></a><span class="lineno"> 2885</span>&#160; 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   params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &amp;inputToCellWeights;</div><div class="line"><a name="l02934"></a><span class="lineno"> 2934</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &amp;inputToOutputWeights;</div><div class="line"><a name="l02935"></a><span class="lineno"> 2935</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &amp;recurrentToForgetWeights;</div><div class="line"><a name="l02936"></a><span class="lineno"> 2936</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &amp;recurrentToCellWeights;</div><div class="line"><a name="l02937"></a><span class="lineno"> 2937</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &amp;recurrentToOutputWeights;</div><div class="line"><a name="l02938"></a><span class="lineno"> 2938</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &amp;forgetGateBias;</div><div class="line"><a name="l02939"></a><span class="lineno"> 2939</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &amp;cellBias;</div><div class="line"><a name="l02940"></a><span class="lineno"> 2940</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &amp;outputGateBias;</div><div class="line"><a name="l02941"></a><span class="lineno"> 2941</span>&#160;</div><div class="line"><a name="l02942"></a><span class="lineno"> 2942</span>&#160;    <span class="comment">// additional params because: descriptor.m_CifgEnabled = false</span></div><div class="line"><a name="l02943"></a><span class="lineno"> 2943</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &amp;inputToInputWeights;</div><div class="line"><a name="l02944"></a><span class="lineno"> 2944</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &amp;recurrentToInputWeights;</div><div class="line"><a name="l02945"></a><span class="lineno"> 2945</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> = &amp;cellToInputWeights;</div><div class="line"><a name="l02946"></a><span class="lineno"> 2946</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &amp;inputGateBias;</div><div class="line"><a name="l02947"></a><span class="lineno"> 2947</span>&#160;</div><div class="line"><a name="l02948"></a><span class="lineno"> 2948</span>&#160;    <span class="comment">// additional params because: descriptor.m_ProjectionEnabled = true</span></div><div class="line"><a name="l02949"></a><span class="lineno"> 2949</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = &amp;projectionWeights;</div><div class="line"><a name="l02950"></a><span class="lineno"> 2950</span>&#160; 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   <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;lstm&quot;</span>);</div><div class="line"><a name="l02961"></a><span class="lineno"> 2961</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> lstmLayer = network-&gt;AddLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l02962"></a><span class="lineno"> 2962</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> scratchBuffer  = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l02963"></a><span class="lineno"> 2963</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut  = network-&gt;AddOutputLayer(1);</div><div class="line"><a name="l02964"></a><span class="lineno"> 2964</span>&#160; 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   <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02970"></a><span class="lineno"> 2970</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02971"></a><span class="lineno"> 2971</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02972"></a><span class="lineno"> 2972</span>&#160;</div><div class="line"><a name="l02973"></a><span class="lineno"> 2973</span>&#160; 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   outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l02978"></a><span class="lineno"> 2978</span>&#160;</div><div class="line"><a name="l02979"></a><span class="lineno"> 2979</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l02980"></a><span class="lineno"> 2980</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l02981"></a><span class="lineno"> 2981</span>&#160;</div><div class="line"><a name="l02982"></a><span class="lineno"> 2982</span>&#160; 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   std::vector&lt;float&gt; inputToForgetWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l03027"></a><span class="lineno"> 3027</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);</div><div class="line"><a name="l03028"></a><span class="lineno"> 3028</span>&#160;</div><div class="line"><a name="l03029"></a><span class="lineno"> 3029</span>&#160;    std::vector&lt;float&gt; inputToCellWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l03030"></a><span class="lineno"> 3030</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);</div><div class="line"><a name="l03031"></a><span class="lineno"> 3031</span>&#160;</div><div class="line"><a name="l03032"></a><span class="lineno"> 3032</span>&#160; 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   std::vector&lt;float&gt; outputGateBiasData = GenerateRandomData&lt;float&gt;(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03046"></a><span class="lineno"> 3046</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(tensorInfo20, outputGateBiasData);</div><div class="line"><a name="l03047"></a><span class="lineno"> 3047</span>&#160;</div><div class="line"><a name="l03048"></a><span class="lineno"> 3048</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x16({numUnits, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03049"></a><span class="lineno"> 3049</span>&#160;    std::vector&lt;float&gt; recurrentToInputWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l03050"></a><span class="lineno"> 3050</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);</div><div class="line"><a name="l03051"></a><span class="lineno"> 3051</span>&#160;</div><div class="line"><a name="l03052"></a><span class="lineno"> 3052</span>&#160;    std::vector&lt;float&gt; recurrentToForgetWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l03053"></a><span class="lineno"> 3053</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);</div><div class="line"><a name="l03054"></a><span class="lineno"> 3054</span>&#160;</div><div class="line"><a name="l03055"></a><span class="lineno"> 3055</span>&#160;    std::vector&lt;float&gt; recurrentToCellWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l03056"></a><span class="lineno"> 3056</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);</div><div class="line"><a name="l03057"></a><span class="lineno"> 3057</span>&#160;</div><div class="line"><a name="l03058"></a><span class="lineno"> 3058</span>&#160;    std::vector&lt;float&gt; recurrentToOutputWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l03059"></a><span class="lineno"> 3059</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);</div><div class="line"><a name="l03060"></a><span class="lineno"> 3060</span>&#160;</div><div class="line"><a name="l03061"></a><span class="lineno"> 3061</span>&#160;    std::vector&lt;float&gt; cellToInputWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03062"></a><span class="lineno"> 3062</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToInputWeights(tensorInfo20, cellToInputWeightsData);</div><div class="line"><a name="l03063"></a><span class="lineno"> 3063</span>&#160;</div><div class="line"><a name="l03064"></a><span class="lineno"> 3064</span>&#160;    std::vector&lt;float&gt; cellToForgetWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03065"></a><span class="lineno"> 3065</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);</div><div class="line"><a name="l03066"></a><span class="lineno"> 3066</span>&#160;</div><div class="line"><a name="l03067"></a><span class="lineno"> 3067</span>&#160;    std::vector&lt;float&gt; cellToOutputWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03068"></a><span class="lineno"> 3068</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToOutputWeights(tensorInfo20,  cellToOutputWeightsData);</div><div class="line"><a name="l03069"></a><span class="lineno"> 3069</span>&#160;</div><div class="line"><a name="l03070"></a><span class="lineno"> 3070</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16x20({outputSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03071"></a><span class="lineno"> 3071</span>&#160;    std::vector&lt;float&gt; projectionWeightsData = GenerateRandomData&lt;float&gt;(tensorInfo16x20.GetNumElements());</div><div class="line"><a name="l03072"></a><span class="lineno"> 3072</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionWeights(tensorInfo16x20, projectionWeightsData);</div><div class="line"><a name="l03073"></a><span class="lineno"> 3073</span>&#160;</div><div class="line"><a name="l03074"></a><span class="lineno"> 3074</span>&#160; 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   <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;lstm&quot;</span>);</div><div class="line"><a name="l03126"></a><span class="lineno"> 3126</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> lstmLayer = network-&gt;AddLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l03127"></a><span class="lineno"> 3127</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> scratchBuffer  = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l03128"></a><span class="lineno"> 3128</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut  = network-&gt;AddOutputLayer(1);</div><div class="line"><a name="l03129"></a><span class="lineno"> 3129</span>&#160; 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   outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l03143"></a><span class="lineno"> 3143</span>&#160;</div><div class="line"><a name="l03144"></a><span class="lineno"> 3144</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l03145"></a><span class="lineno"> 3145</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l03146"></a><span class="lineno"> 3146</span>&#160;</div><div class="line"><a name="l03147"></a><span class="lineno"> 3147</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(scratchBuffer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l03148"></a><span class="lineno"> 3148</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(lstmTensorInfoScratchBuff);</div><div class="line"><a name="l03149"></a><span class="lineno"> 3149</span>&#160;</div><div class="line"><a name="l03150"></a><span class="lineno"> 3150</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l03151"></a><span class="lineno"> 3151</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l03152"></a><span class="lineno"> 3152</span>&#160;</div><div class="line"><a name="l03153"></a><span class="lineno"> 3153</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l03154"></a><span class="lineno"> 3154</span>&#160;    lstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l03155"></a><span class="lineno"> 3155</span>&#160;</div><div class="line"><a name="l03156"></a><span class="lineno"> 3156</span>&#160; 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   deserializedNetwork-&gt;Accept(checker);</div><div class="line"><a name="l03169"></a><span class="lineno"> 3169</span>&#160;}</div><div class="line"><a name="l03170"></a><span class="lineno"> 3170</span>&#160;</div><div class="line"><a name="l03171"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#ab1042b9567ba6e028411498f0387fbbd"> 3171</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(EnsureLstmLayersBackwardCompatibility)</div><div class="line"><a name="l03172"></a><span class="lineno"> 3172</span>&#160;{</div><div class="line"><a name="l03173"></a><span class="lineno"> 3173</span>&#160;    <span class="comment">// The hex data below is a flat buffer containing a lstm layer with no Cifg, with peephole and projection</span></div><div class="line"><a name="l03174"></a><span class="lineno"> 3174</span>&#160;    <span class="comment">// enabled. That data was obtained before additional layer normalization parameters where added to the</span></div><div class="line"><a name="l03175"></a><span class="lineno"> 3175</span>&#160;    <span class="comment">// lstm serializer. That way it can be tested if a lstm model with the old parameter configuration can</span></div><div class="line"><a name="l03176"></a><span class="lineno"> 3176</span>&#160;    <span class="comment">// still be loaded</span></div><div class="line"><a name="l03177"></a><span class="lineno"> 3177</span>&#160;    <span class="keyword">const</span> std::vector&lt;uint8_t&gt; lstmNoCifgWithPeepholeAndProjectionModel =</div><div class="line"><a name="l03178"></a><span class="lineno"> 3178</span>&#160;    {</div><div class="line"><a name="l03179"></a><span class="lineno"> 3179</span>&#160;        0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l03180"></a><span class="lineno"> 3180</span>&#160;        0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03181"></a><span class="lineno"> 3181</span>&#160;        0xDC, 0x29, 0x00, 0x00, 0x38, 0x29, 0x00, 0x00, 0xB4, 0x28, 0x00, 0x00, 0x94, 0x01, 0x00, 0x00, 0x3C, 0x01,</div><div class="line"><a name="l03182"></a><span class="lineno"> 3182</span>&#160;        0x00, 0x00, 0xE0, 0x00, 0x00, 0x00, 0x84, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03183"></a><span class="lineno"> 3183</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l03184"></a><span class="lineno"> 3184</span>&#160;        0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x70, 0xD6, 0xFF, 0xFF,</div><div class="line"><a name="l03185"></a><span class="lineno"> 3185</span>&#160;        0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x06, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x88, 0xD7,</div><div class="line"><a name="l03186"></a><span class="lineno"> 3186</span>&#160;        0xFF, 0xFF, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xF6, 0xD6, 0xFF, 0xFF, 0x07, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03187"></a><span class="lineno"> 3187</span>&#160;        0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03188"></a><span class="lineno"> 3188</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03189"></a><span class="lineno"> 3189</span>&#160;        0xE8, 0xD7, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xC8, 0xD6, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l03190"></a><span class="lineno"> 3190</span>&#160;        0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x5E, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xE0, 0xD7, 0xFF, 0xFF,</div><div class="line"><a name="l03191"></a><span class="lineno"> 3191</span>&#160;        0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x4E, 0xD7, 0xFF, 0xFF, 0x06, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l03192"></a><span class="lineno"> 3192</span>&#160;        0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03193"></a><span class="lineno"> 3193</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xD8,</div><div class="line"><a name="l03194"></a><span class="lineno"> 3194</span>&#160;        0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x20, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,</div><div class="line"><a name="l03195"></a><span class="lineno"> 3195</span>&#160;        0x04, 0x00, 0x00, 0x00, 0xB6, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x38, 0xD8, 0xFF, 0xFF, 0x08, 0x00,</div><div class="line"><a name="l03196"></a><span class="lineno"> 3196</span>&#160;        0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xA6, 0xD7, 0xFF, 0xFF, 0x05, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03197"></a><span class="lineno"> 3197</span>&#160;        0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03198"></a><span class="lineno"> 3198</span>&#160;        0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xD8, 0xFF, 0xFF,</div><div class="line"><a name="l03199"></a><span class="lineno"> 3199</span>&#160;        0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x78, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00,</div><div class="line"><a name="l03200"></a><span class="lineno"> 3200</span>&#160;        0x00, 0x00, 0x0E, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x16, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03201"></a><span class="lineno"> 3201</span>&#160;        0xFA, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l03202"></a><span class="lineno"> 3202</span>&#160;        0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03203"></a><span class="lineno"> 3203</span>&#160;        0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xD8, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03204"></a><span class="lineno"> 3204</span>&#160;        0x00, 0x00, 0x6C, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x23, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l03205"></a><span class="lineno"> 3205</span>&#160;        0x12, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0xE0, 0x25, 0x00, 0x00, 0xD0, 0x25,</div><div class="line"><a name="l03206"></a><span class="lineno"> 3206</span>&#160;        0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0x00, 0x48, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00,</div><div class="line"><a name="l03207"></a><span class="lineno"> 3207</span>&#160;        0x10, 0x00, 0x14, 0x00, 0x18, 0x00, 0x1C, 0x00, 0x20, 0x00, 0x24, 0x00, 0x28, 0x00, 0x2C, 0x00, 0x30, 0x00,</div><div class="line"><a name="l03208"></a><span class="lineno"> 3208</span>&#160;        0x34, 0x00, 0x38, 0x00, 0x3C, 0x00, 0x40, 0x00, 0x44, 0x00, 0x26, 0x00, 0x00, 0x00, 0xC4, 0x23, 0x00, 0x00,</div><div class="line"><a name="l03209"></a><span class="lineno"> 3209</span>&#160;        0xF8, 0x21, 0x00, 0x00, 0x2C, 0x20, 0x00, 0x00, 0xF0, 0x1A, 0x00, 0x00, 0xB4, 0x15, 0x00, 0x00, 0x78, 0x10,</div><div class="line"><a name="l03210"></a><span class="lineno"> 3210</span>&#160;        0x00, 0x00, 0xF0, 0x0F, 0x00, 0x00, 0x68, 0x0F, 0x00, 0x00, 0xE0, 0x0E, 0x00, 0x00, 0x14, 0x0D, 0x00, 0x00,</div><div class="line"><a name="l03211"></a><span class="lineno"> 3211</span>&#160;        0xD8, 0x07, 0x00, 0x00, 0x50, 0x07, 0x00, 0x00, 0xC8, 0x06, 0x00, 0x00, 0x8C, 0x01, 0x00, 0x00, 0x14, 0x01,</div><div class="line"><a name="l03212"></a><span class="lineno"> 3212</span>&#160;        0x00, 0x00, 0x8C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xEE, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03,</div><div class="line"><a name="l03213"></a><span class="lineno"> 3213</span>&#160;        0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00,</div><div class="line"><a name="l03214"></a><span class="lineno"> 3214</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03215"></a><span class="lineno"> 3215</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03216"></a><span class="lineno"> 3216</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03217"></a><span class="lineno"> 3217</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03218"></a><span class="lineno"> 3218</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01,</div><div class="line"><a name="l03219"></a><span class="lineno"> 3219</span>&#160;        0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x72, 0xD8,</div><div class="line"><a name="l03220"></a><span class="lineno"> 3220</span>&#160;        0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x82, 0xD9, 0xFF, 0xFF,</div><div class="line"><a name="l03221"></a><span class="lineno"> 3221</span>&#160;        0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03222"></a><span class="lineno"> 3222</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03223"></a><span class="lineno"> 3223</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03224"></a><span class="lineno"> 3224</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03225"></a><span class="lineno"> 3225</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDE, 0xD8,</div><div class="line"><a name="l03226"></a><span class="lineno"> 3226</span>&#160;        0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03227"></a><span class="lineno"> 3227</span>&#160;        0x14, 0x00, 0x00, 0x00, 0xF6, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x54, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l03228"></a><span class="lineno"> 3228</span>&#160;        0x00, 0x00, 0x06, 0xDA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03229"></a><span class="lineno"> 3229</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03230"></a><span class="lineno"> 3230</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03231"></a><span class="lineno"> 3231</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03232"></a><span class="lineno"> 3232</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xD9, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03233"></a><span class="lineno"> 3233</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x6A, 0xD9, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l03234"></a><span class="lineno"> 3234</span>&#160;        0x00, 0x03, 0x14, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x7A, 0xDA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03235"></a><span class="lineno"> 3235</span>&#160;        0x40, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03236"></a><span class="lineno"> 3236</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03237"></a><span class="lineno"> 3237</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03238"></a><span class="lineno"> 3238</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03239"></a><span class="lineno"> 3239</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03240"></a><span class="lineno"> 3240</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03241"></a><span class="lineno"> 3241</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03242"></a><span class="lineno"> 3242</span>&#160; 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       0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03717"></a><span class="lineno"> 3717</span>&#160;        0x14, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x6E, 0xFB, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0xA4, 0x01,</div><div class="line"><a name="l03718"></a><span class="lineno"> 3718</span>&#160;        0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x7E, 0xFC, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x64, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03719"></a><span class="lineno"> 3719</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03720"></a><span class="lineno"> 3720</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03721"></a><span class="lineno"> 3721</span>&#160;        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03722"></a><span class="lineno"> 3722</span>&#160; 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   <span class="comment">// additional params because: descriptor.m_ProjectionEnabled = true</span></div><div class="line"><a name="l03881"></a><span class="lineno"> 3881</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a>        = &amp;projectionWeights;</div><div class="line"><a name="l03882"></a><span class="lineno"> 3882</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a>           = &amp;projectionBias;</div><div class="line"><a name="l03883"></a><span class="lineno"> 3883</span>&#160;</div><div class="line"><a name="l03884"></a><span class="lineno"> 3884</span>&#160;    <span class="comment">// additional params because: descriptor.m_PeepholeEnabled = true</span></div><div class="line"><a name="l03885"></a><span class="lineno"> 3885</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a>      = &amp;cellToForgetWeights;</div><div class="line"><a name="l03886"></a><span class="lineno"> 3886</span>&#160;    params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a>      = &amp;cellToOutputWeights;</div><div class="line"><a name="l03887"></a><span class="lineno"> 3887</span>&#160;</div><div class="line"><a name="l03888"></a><span class="lineno"> 3888</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;lstm&quot;</span>);</div><div class="line"><a name="l03889"></a><span class="lineno"> 3889</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03890"></a><span class="lineno"> 3890</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03891"></a><span class="lineno"> 3891</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03892"></a><span class="lineno"> 3892</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03893"></a><span class="lineno"> 3893</span>&#160;</div><div class="line"><a name="l03894"></a><span class="lineno"> 3894</span>&#160;    VerifyLstmLayer checker(</div><div class="line"><a name="l03895"></a><span class="lineno"> 3895</span>&#160;            layerName,</div><div class="line"><a name="l03896"></a><span class="lineno"> 3896</span>&#160;            {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l03897"></a><span class="lineno"> 3897</span>&#160;            {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l03898"></a><span class="lineno"> 3898</span>&#160;            descriptor,</div><div class="line"><a name="l03899"></a><span class="lineno"> 3899</span>&#160;            params);</div><div class="line"><a name="l03900"></a><span class="lineno"> 3900</span>&#160;    deserializedNetwork-&gt;Accept(checker);</div><div class="line"><a name="l03901"></a><span class="lineno"> 3901</span>&#160;}</div><div class="line"><a name="l03902"></a><span class="lineno"> 3902</span>&#160;<span class="keyword">class </span>VerifyQuantizedLstmLayer : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l03903"></a><span class="lineno"> 3903</span>&#160;{</div><div class="line"><a name="l03904"></a><span class="lineno"> 3904</span>&#160;</div><div class="line"><a name="l03905"></a><span class="lineno"> 3905</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l03906"></a><span class="lineno"> 3906</span>&#160;    VerifyQuantizedLstmLayer(<span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l03907"></a><span class="lineno"> 3907</span>&#160;                             <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; inputInfos,</div><div class="line"><a name="l03908"></a><span class="lineno"> 3908</span>&#160;                             <span class="keyword">const</span> std::vector&lt;armnn::TensorInfo&gt;&amp; outputInfos,</div><div class="line"><a name="l03909"></a><span class="lineno"> 3909</span>&#160;                             <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a>&amp; inputParams)</div><div class="line"><a name="l03910"></a><span class="lineno"> 3910</span>&#160;        : LayerVerifierBase(layerName, inputInfos, outputInfos), m_InputParams(inputParams) {}</div><div class="line"><a name="l03911"></a><span class="lineno"> 3911</span>&#160;</div><div class="line"><a name="l03912"></a><span class="lineno"> 3912</span>&#160;    <span class="keywordtype">void</span> VisitQuantizedLstmLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l03913"></a><span class="lineno"> 3913</span>&#160;                                 <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a>&amp; params,</div><div class="line"><a name="l03914"></a><span class="lineno"> 3914</span>&#160;                                 <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l03915"></a><span class="lineno"> 3915</span>&#160;    {</div><div class="line"><a name="l03916"></a><span class="lineno"> 3916</span>&#160;        VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l03917"></a><span class="lineno"> 3917</span>&#160;        VerifyInputParameters(params);</div><div class="line"><a name="l03918"></a><span class="lineno"> 3918</span>&#160;    }</div><div class="line"><a name="l03919"></a><span class="lineno"> 3919</span>&#160;</div><div class="line"><a name="l03920"></a><span class="lineno"> 3920</span>&#160;<span class="keyword">protected</span>:</div><div class="line"><a name="l03921"></a><span class="lineno"> 3921</span>&#160;    <span class="keywordtype">void</span> VerifyInputParameters(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a>&amp; params)</div><div class="line"><a name="l03922"></a><span class="lineno"> 3922</span>&#160;    {</div><div class="line"><a name="l03923"></a><span class="lineno"> 3923</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_InputToInputWeights&quot;</span>,</div><div class="line"><a name="l03924"></a><span class="lineno"> 3924</span>&#160;                           m_InputParams.m_InputToInputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a>);</div><div class="line"><a name="l03925"></a><span class="lineno"> 3925</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_InputToForgetWeights&quot;</span>,</div><div class="line"><a name="l03926"></a><span class="lineno"> 3926</span>&#160;                           m_InputParams.m_InputToForgetWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a>);</div><div class="line"><a name="l03927"></a><span class="lineno"> 3927</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_InputToCellWeights&quot;</span>,</div><div class="line"><a name="l03928"></a><span class="lineno"> 3928</span>&#160;                           m_InputParams.m_InputToCellWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a>);</div><div class="line"><a name="l03929"></a><span class="lineno"> 3929</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_InputToOutputWeights&quot;</span>,</div><div class="line"><a name="l03930"></a><span class="lineno"> 3930</span>&#160;                           m_InputParams.m_InputToOutputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a>);</div><div class="line"><a name="l03931"></a><span class="lineno"> 3931</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_RecurrentToInputWeights&quot;</span>,</div><div class="line"><a name="l03932"></a><span class="lineno"> 3932</span>&#160;                           m_InputParams.m_RecurrentToInputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a>);</div><div class="line"><a name="l03933"></a><span class="lineno"> 3933</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_RecurrentToForgetWeights&quot;</span>,</div><div class="line"><a name="l03934"></a><span class="lineno"> 3934</span>&#160;                           m_InputParams.m_RecurrentToForgetWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a>);</div><div class="line"><a name="l03935"></a><span class="lineno"> 3935</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_RecurrentToCellWeights&quot;</span>,</div><div class="line"><a name="l03936"></a><span class="lineno"> 3936</span>&#160;                           m_InputParams.m_RecurrentToCellWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>);</div><div class="line"><a name="l03937"></a><span class="lineno"> 3937</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_RecurrentToOutputWeights&quot;</span>,</div><div class="line"><a name="l03938"></a><span class="lineno"> 3938</span>&#160;                           m_InputParams.m_RecurrentToOutputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a>);</div><div class="line"><a name="l03939"></a><span class="lineno"> 3939</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_InputGateBias&quot;</span>,</div><div class="line"><a name="l03940"></a><span class="lineno"> 3940</span>&#160;                           m_InputParams.m_InputGateBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a>);</div><div class="line"><a name="l03941"></a><span class="lineno"> 3941</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_ForgetGateBias&quot;</span>,</div><div class="line"><a name="l03942"></a><span class="lineno"> 3942</span>&#160;                           m_InputParams.m_ForgetGateBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a>);</div><div class="line"><a name="l03943"></a><span class="lineno"> 3943</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_CellBias&quot;</span>,</div><div class="line"><a name="l03944"></a><span class="lineno"> 3944</span>&#160;                           m_InputParams.m_CellBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a>);</div><div class="line"><a name="l03945"></a><span class="lineno"> 3945</span>&#160;        VerifyConstTensors(<span class="stringliteral">&quot;m_OutputGateBias&quot;</span>,</div><div class="line"><a name="l03946"></a><span class="lineno"> 3946</span>&#160;                           m_InputParams.m_OutputGateBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a>);</div><div class="line"><a name="l03947"></a><span class="lineno"> 3947</span>&#160;    }</div><div class="line"><a name="l03948"></a><span class="lineno"> 3948</span>&#160;</div><div class="line"><a name="l03949"></a><span class="lineno"> 3949</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l03950"></a><span class="lineno"> 3950</span>&#160;    <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a> m_InputParams;</div><div class="line"><a name="l03951"></a><span class="lineno"> 3951</span>&#160;};</div><div class="line"><a name="l03952"></a><span class="lineno"> 3952</span>&#160;</div><div class="line"><a name="l03953"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a1cb7aec5bf87ff679cfd0ee9aa7d41c2"> 3953</a></span>&#160;<a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeQuantizedLstm)</div><div class="line"><a name="l03954"></a><span class="lineno"> 3954</span>&#160;{</div><div class="line"><a name="l03955"></a><span class="lineno"> 3955</span>&#160;    <span class="keyword">const</span> uint32_t batchSize = 1;</div><div class="line"><a name="l03956"></a><span class="lineno"> 3956</span>&#160;    <span class="keyword">const</span> uint32_t inputSize = 2;</div><div class="line"><a name="l03957"></a><span class="lineno"> 3957</span>&#160;    <span class="keyword">const</span> uint32_t numUnits = 4;</div><div class="line"><a name="l03958"></a><span class="lineno"> 3958</span>&#160;    <span class="keyword">const</span> uint32_t outputSize = numUnits;</div><div class="line"><a name="l03959"></a><span class="lineno"> 3959</span>&#160;</div><div class="line"><a name="l03960"></a><span class="lineno"> 3960</span>&#160;    <span class="comment">// Scale/Offset for input/output, cellState In/Out, weights, bias</span></div><div class="line"><a name="l03961"></a><span class="lineno"> 3961</span>&#160;    <span class="keywordtype">float</span> inputOutputScale = 0.0078125f;</div><div class="line"><a name="l03962"></a><span class="lineno"> 3962</span>&#160;    int32_t inputOutputOffset = 128;</div><div class="line"><a name="l03963"></a><span class="lineno"> 3963</span>&#160;</div><div class="line"><a name="l03964"></a><span class="lineno"> 3964</span>&#160;    <span class="keywordtype">float</span> cellStateScale = 0.00048828125f;</div><div class="line"><a name="l03965"></a><span class="lineno"> 3965</span>&#160;    int32_t cellStateOffset = 0;</div><div class="line"><a name="l03966"></a><span class="lineno"> 3966</span>&#160;</div><div class="line"><a name="l03967"></a><span class="lineno"> 3967</span>&#160;    <span class="keywordtype">float</span> weightsScale = 0.00408021f;</div><div class="line"><a name="l03968"></a><span class="lineno"> 3968</span>&#160;    int32_t weightsOffset = 100;</div><div class="line"><a name="l03969"></a><span class="lineno"> 3969</span>&#160;</div><div class="line"><a name="l03970"></a><span class="lineno"> 3970</span>&#160;    <span class="keywordtype">float</span> biasScale = 3.1876640625e-05f;</div><div class="line"><a name="l03971"></a><span class="lineno"> 3971</span>&#160;    int32_t biasOffset = 0;</div><div class="line"><a name="l03972"></a><span class="lineno"> 3972</span>&#160;</div><div class="line"><a name="l03973"></a><span class="lineno"> 3973</span>&#160;    <span class="comment">// The shape of weight data is {outputSize, inputSize} = {4, 2}</span></div><div class="line"><a name="l03974"></a><span class="lineno"> 3974</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToInputWeightsShape = {4, 2};</div><div class="line"><a name="l03975"></a><span class="lineno"> 3975</span>&#160;    std::vector&lt;uint8_t&gt; inputToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l03976"></a><span class="lineno"> 3976</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToInputWeightsInfo(inputToInputWeightsShape,</div><div class="line"><a name="l03977"></a><span class="lineno"> 3977</span>&#160;                                              <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l03978"></a><span class="lineno"> 3978</span>&#160;                                              weightsScale,</div><div class="line"><a name="l03979"></a><span class="lineno"> 3979</span>&#160;                                              weightsOffset);</div><div class="line"><a name="l03980"></a><span class="lineno"> 3980</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(inputToInputWeightsInfo, inputToInputWeightsData);</div><div class="line"><a name="l03981"></a><span class="lineno"> 3981</span>&#160;</div><div class="line"><a name="l03982"></a><span class="lineno"> 3982</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToForgetWeightsShape = {4, 2};</div><div class="line"><a name="l03983"></a><span class="lineno"> 3983</span>&#160;    std::vector&lt;uint8_t&gt; inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l03984"></a><span class="lineno"> 3984</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToForgetWeightsInfo(inputToForgetWeightsShape,</div><div class="line"><a name="l03985"></a><span class="lineno"> 3985</span>&#160;                                               <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l03986"></a><span class="lineno"> 3986</span>&#160;                                               weightsScale,</div><div class="line"><a name="l03987"></a><span class="lineno"> 3987</span>&#160;                                               weightsOffset);</div><div class="line"><a name="l03988"></a><span class="lineno"> 3988</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(inputToForgetWeightsInfo, inputToForgetWeightsData);</div><div class="line"><a name="l03989"></a><span class="lineno"> 3989</span>&#160;</div><div class="line"><a name="l03990"></a><span class="lineno"> 3990</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToCellWeightsShape = {4, 2};</div><div class="line"><a name="l03991"></a><span class="lineno"> 3991</span>&#160;    std::vector&lt;uint8_t&gt; inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l03992"></a><span class="lineno"> 3992</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToCellWeightsInfo(inputToCellWeightsShape,</div><div class="line"><a name="l03993"></a><span class="lineno"> 3993</span>&#160;                                             <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l03994"></a><span class="lineno"> 3994</span>&#160;                                             weightsScale,</div><div class="line"><a name="l03995"></a><span class="lineno"> 3995</span>&#160;                                             weightsOffset);</div><div class="line"><a name="l03996"></a><span class="lineno"> 3996</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(inputToCellWeightsInfo, inputToCellWeightsData);</div><div class="line"><a name="l03997"></a><span class="lineno"> 3997</span>&#160;</div><div class="line"><a name="l03998"></a><span class="lineno"> 3998</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToOutputWeightsShape = {4, 2};</div><div class="line"><a name="l03999"></a><span class="lineno"> 3999</span>&#160;    std::vector&lt;uint8_t&gt; inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l04000"></a><span class="lineno"> 4000</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToOutputWeightsInfo(inputToOutputWeightsShape,</div><div class="line"><a name="l04001"></a><span class="lineno"> 4001</span>&#160;                                               <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l04002"></a><span class="lineno"> 4002</span>&#160;                                               weightsScale,</div><div class="line"><a name="l04003"></a><span class="lineno"> 4003</span>&#160;                                               weightsOffset);</div><div class="line"><a name="l04004"></a><span class="lineno"> 4004</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(inputToOutputWeightsInfo, inputToOutputWeightsData);</div><div class="line"><a name="l04005"></a><span class="lineno"> 4005</span>&#160;</div><div class="line"><a name="l04006"></a><span class="lineno"> 4006</span>&#160;    <span class="comment">// The shape of recurrent weight data is {outputSize, outputSize} = {4, 4}</span></div><div class="line"><a name="l04007"></a><span class="lineno"> 4007</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToInputWeightsShape = {4, 4};</div><div class="line"><a name="l04008"></a><span class="lineno"> 4008</span>&#160;    std::vector&lt;uint8_t&gt; recurrentToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};</div><div class="line"><a name="l04009"></a><span class="lineno"> 4009</span>&#160; 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                                                  weightsOffset);</div><div class="line"><a name="l04021"></a><span class="lineno"> 4021</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(recurrentToForgetWeightsInfo, recurrentToForgetWeightsData);</div><div class="line"><a name="l04022"></a><span class="lineno"> 4022</span>&#160;</div><div class="line"><a name="l04023"></a><span class="lineno"> 4023</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToCellWeightsShape = {4, 4};</div><div class="line"><a name="l04024"></a><span class="lineno"> 4024</span>&#160;    std::vector&lt;uint8_t&gt; recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};</div><div class="line"><a name="l04025"></a><span class="lineno"> 4025</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentToCellWeightsInfo(recurrentToCellWeightsShape,</div><div class="line"><a name="l04026"></a><span class="lineno"> 4026</span>&#160;                                                 <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l04027"></a><span class="lineno"> 4027</span>&#160;                                                 weightsScale,</div><div class="line"><a name="l04028"></a><span class="lineno"> 4028</span>&#160;                                                 weightsOffset);</div><div class="line"><a name="l04029"></a><span class="lineno"> 4029</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(recurrentToCellWeightsInfo, recurrentToCellWeightsData);</div><div class="line"><a name="l04030"></a><span class="lineno"> 4030</span>&#160;</div><div class="line"><a name="l04031"></a><span class="lineno"> 4031</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToOutputWeightsShape = {4, 4};</div><div class="line"><a name="l04032"></a><span class="lineno"> 4032</span>&#160; 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   params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &amp;forgetGateBias;</div><div class="line"><a name="l04083"></a><span class="lineno"> 4083</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &amp;cellBias;</div><div class="line"><a name="l04084"></a><span class="lineno"> 4084</span>&#160;    params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &amp;outputGateBias;</div><div class="line"><a name="l04085"></a><span class="lineno"> 4085</span>&#160;</div><div class="line"><a name="l04086"></a><span class="lineno"> 4086</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l04087"></a><span class="lineno"> 4087</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l04088"></a><span class="lineno"> 4088</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network-&gt;AddInputLayer(1);</div><div class="line"><a name="l04089"></a><span class="lineno"> 4089</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network-&gt;AddInputLayer(2);</div><div class="line"><a name="l04090"></a><span class="lineno"> 4090</span>&#160;    <span class="keyword">const</span> std::string layerName(<span class="stringliteral">&quot;QuantizedLstm&quot;</span>);</div><div class="line"><a name="l04091"></a><span class="lineno"> 4091</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> quantizedLstmLayer = network-&gt;AddQuantizedLstmLayer(params, layerName.c_str());</div><div class="line"><a name="l04092"></a><span class="lineno"> 4092</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l04093"></a><span class="lineno"> 4093</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network-&gt;AddOutputLayer(1);</div><div class="line"><a name="l04094"></a><span class="lineno"> 4094</span>&#160;</div><div class="line"><a name="l04095"></a><span class="lineno"> 4095</span>&#160;    <span class="comment">// Connect up</span></div><div class="line"><a name="l04096"></a><span class="lineno"> 4096</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize },</div><div class="line"><a name="l04097"></a><span class="lineno"> 4097</span>&#160;                                      <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l04098"></a><span class="lineno"> 4098</span>&#160;                                      inputOutputScale,</div><div class="line"><a name="l04099"></a><span class="lineno"> 4099</span>&#160;                                      inputOutputOffset);</div><div class="line"><a name="l04100"></a><span class="lineno"> 4100</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits },</div><div class="line"><a name="l04101"></a><span class="lineno"> 4101</span>&#160;                                          <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l04102"></a><span class="lineno"> 4102</span>&#160;                                          cellStateScale,</div><div class="line"><a name="l04103"></a><span class="lineno"> 4103</span>&#160;                                          cellStateOffset);</div><div class="line"><a name="l04104"></a><span class="lineno"> 4104</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize },</div><div class="line"><a name="l04105"></a><span class="lineno"> 4105</span>&#160;                                            <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l04106"></a><span class="lineno"> 4106</span>&#160;                                            inputOutputScale,</div><div class="line"><a name="l04107"></a><span class="lineno"> 4107</span>&#160;                                            inputOutputOffset);</div><div class="line"><a name="l04108"></a><span class="lineno"> 4108</span>&#160;</div><div class="line"><a name="l04109"></a><span class="lineno"> 4109</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l04110"></a><span class="lineno"> 4110</span>&#160;    inputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l04111"></a><span class="lineno"> 4111</span>&#160;</div><div class="line"><a name="l04112"></a><span class="lineno"> 4112</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l04113"></a><span class="lineno"> 4113</span>&#160;    cellStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l04114"></a><span class="lineno"> 4114</span>&#160;</div><div class="line"><a name="l04115"></a><span class="lineno"> 4115</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l04116"></a><span class="lineno"> 4116</span>&#160;    outputStateIn-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l04117"></a><span class="lineno"> 4117</span>&#160;</div><div class="line"><a name="l04118"></a><span class="lineno"> 4118</span>&#160;    quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l04119"></a><span class="lineno"> 4119</span>&#160;    quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l04120"></a><span class="lineno"> 4120</span>&#160;</div><div class="line"><a name="l04121"></a><span class="lineno"> 4121</span>&#160;    quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l04122"></a><span class="lineno"> 4122</span>&#160;    quantizedLstmLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l04123"></a><span class="lineno"> 4123</span>&#160;</div><div class="line"><a name="l04124"></a><span class="lineno"> 4124</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l04125"></a><span class="lineno"> 4125</span>&#160;    <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l04126"></a><span class="lineno"> 4126</span>&#160;</div><div class="line"><a name="l04127"></a><span class="lineno"> 4127</span>&#160;    VerifyQuantizedLstmLayer checker(layerName,</div><div class="line"><a name="l04128"></a><span class="lineno"> 4128</span>&#160;                                     {inputTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l04129"></a><span class="lineno"> 4129</span>&#160;                                     {cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l04130"></a><span class="lineno"> 4130</span>&#160;                                     params);</div><div class="line"><a name="l04131"></a><span class="lineno"> 4131</span>&#160;</div><div class="line"><a name="l04132"></a><span class="lineno"> 4132</span>&#160;    deserializedNetwork-&gt;Accept(checker);</div><div class="line"><a name="l04133"></a><span class="lineno"> 4133</span>&#160;}</div><div class="line"><a name="l04134"></a><span class="lineno"> 4134</span>&#160;</div><div class="line"><a name="l04135"></a><span class="lineno"> 4135</span>&#160;<a class="code" href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a>()</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_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00428">Descriptors.hpp:428</a></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#l00438">Descriptors.hpp:438</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58ca8768a6821cd735aea4f5b0df88c1fc6a"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca8768a6821cd735aea4f5b0df88c1fc6a">armnn::ComparisonOperation::Greater</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_ac2dac3b61c94de52093616be4ab17f8d"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">armnn::IConnectableLayer::GetNumOutputSlots</a></div><div class="ttdeci">virtual unsigned int GetNumOutputSlots() const =0</div><div class="ttdoc">Returns the number of connectable output slots. </div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">armnn::LstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00871">Descriptors.hpp:871</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00440">Descriptors.hpp:440</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ab03e6e1514f74427916c892f473fe04c"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">armnn::LstmInputParams::m_ProjectionWeights</a></div><div class="ttdeci">const ConstTensor * m_ProjectionWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00055">LstmParams.hpp:55</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a855293b1be0581fb61ef6a1c5b027d0f"><div class="ttname"><a href="namespacearmnn.xhtml#a855293b1be0581fb61ef6a1c5b027d0f">armnn::Dequantize</a></div><div class="ttdeci">float Dequantize(QuantizedType value, float scale, int32_t offset)</div><div class="ttdoc">Dequantize an 8-bit data type into a floating point data type. </div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8cpp_source.xhtml#l00047">TypesUtils.cpp:47</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a49e11acda22742cbaf6f1b259ead0d84"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">armnn::QuantizedLstmInputParams::m_InputToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00035">QuantizedLstmParams.hpp:35</a></div></div>
<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a></div><div class="ttdoc">A ViewsDescriptor for the SplitterLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00190">Descriptors.hpp:190</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00061">INetwork.hpp:61</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ab509802c659de19929f18bad14a35c58"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ab509802c659de19929f18bad14a35c58">armnn::DetectionPostProcessDescriptor::m_ScaleW</a></div><div class="ttdeci">float m_ScaleW</div><div class="ttdoc">Center size encoding scale weight. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00545">Descriptors.hpp:545</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a4a9d678146f572808a361dbdc5489f38"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">armnn::LstmInputParams::m_CellBias</a></div><div class="ttdeci">const ConstTensor * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00053">LstmParams.hpp:53</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::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#l00490">Descriptors.hpp:490</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a9c2cba04b6d7ace4fc2a2436b82a5a63"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">armnn::IConnectableLayer::GetNumInputSlots</a></div><div class="ttdeci">virtual unsigned int GetNumInputSlots() const =0</div><div class="ttdoc">Returns the number of connectable input slots. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_a044ea0cc993d4d1fbe4ec877b17b8d39"><div class="ttname"><a href="namespacearmnn.xhtml#a044ea0cc993d4d1fbe4ec877b17b8d39">armnn::Slice</a></div><div class="ttdeci">void Slice(const TensorInfo &amp;inputInfo, const SliceDescriptor &amp;descriptor, const void *inputData, void *outputData, unsigned int dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_slice_8cpp_source.xhtml#l00016">Slice.cpp:16</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a></div><div class="ttdoc">A TransposeConvolution2dDescriptor for the TransposeConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01079">Descriptors.hpp:1079</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a4a9d678146f572808a361dbdc5489f38"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">armnn::QuantizedLstmInputParams::m_CellBias</a></div><div class="ttdeci">const ConstTensor * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00045">QuantizedLstmParams.hpp:45</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00088">Tensor.hpp:88</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a56b81ca8ba4b4937e0787e4951f043fc"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">armnn::QuantizedLstmInputParams::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00041">QuantizedLstmParams.hpp:41</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00480">Descriptors.hpp:480</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a86e88bef0df4df96df752b4b8955a3af"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">armnn::LstmDescriptor::m_ClippingThresProj</a></div><div class="ttdeci">float m_ClippingThresProj</div><div class="ttdoc">Clipping threshold value for the projection. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00865">Descriptors.hpp:865</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt; armnn::ConstTensor &gt;</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58caf5f286e73bda105e538310b3190f75c5"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58caf5f286e73bda105e538310b3190f75c5">armnn::ComparisonOperation::Equal</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3"><div class="ttname"><a href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a></div></div>
<div class="ttc" id="structarmnn_1_1_reshape_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a></div><div class="ttdoc">A ReshapeDescriptor for the ReshapeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00758">Descriptors.hpp:758</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a374120de442fe42c26536bb4f1e2a5a1"><div class="ttname"><a href="namespacearmnn.xhtml#a374120de442fe42c26536bb4f1e2a5a1">armnn::ArgMinMax</a></div><div class="ttdeci">void ArgMinMax(Decoder&lt; float &gt; &amp;in, int32_t *out, const TensorInfo &amp;inputTensorInfo, const TensorInfo &amp;outputTensorInfo, ArgMinMaxFunction function, int axis)</div><div class="ttdef"><b>Definition:</b> <a href="_arg_min_max_8cpp_source.xhtml#l00015">ArgMinMax.cpp:15</a></div></div>
<div class="ttc" id="namespacearmnn_serializer_xhtml"><div class="ttname"><a href="namespacearmnn_serializer.xhtml">armnnSerializer</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_serializer_8hpp_source.xhtml#l00011">ISerializer.hpp:11</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_afe204ca375b74e9a72640c9227918d0a"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">armnn::LstmInputParams::m_CellToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00050">LstmParams.hpp:50</a></div></div>
<div class="ttc" id="_deprecated_8hpp_xhtml_ab66a241a0ed3ee89c866e777b035d0ed"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_BEGIN</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00033">Deprecated.hpp:33</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_ae83131e16df1cace69395a5f99bc5ecb"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">armnn::QuantizedLstmInputParams::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00039">QuantizedLstmParams.hpp:39</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00492">Descriptors.hpp:492</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#l00053">Tensor.hpp:53</a></div></div>
<div class="ttc" id="structarmnn_1_1_comparison_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a></div><div class="ttdoc">A ComparisonDescriptor for the ComparisonLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00062">Descriptors.hpp:62</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ae64523937ea910030ad66fee6fddd51f"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae64523937ea910030ad66fee6fddd51f">armnn::DetectionPostProcessDescriptor::m_ScaleX</a></div><div class="ttdeci">float m_ScaleX</div><div class="ttdoc">Center size encoding scale x. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00541">Descriptors.hpp:541</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_bilinear_descriptor_xhtml_adcf5037208faac36c0788239a073f75c"><div class="ttname"><a href="structarmnn_1_1_resize_bilinear_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">armnn::ResizeBilinearDescriptor::m_TargetWidth</a></div><div class="ttdeci">uint32_t m_TargetWidth</div><div class="ttdoc">Target width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00716">Descriptors.hpp:716</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#l00388">Descriptors.hpp:388</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00392">Descriptors.hpp:392</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_quantized_lstm_input_params_xhtml_ace7a1f1f1041b412b7d8ef82b95ff352"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">armnn::QuantizedLstmInputParams::m_ForgetGateBias</a></div><div class="ttdeci">const ConstTensor * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00044">QuantizedLstmParams.hpp:44</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00474">Descriptors.hpp:474</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::TransposeConvolution2dDescriptor::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#l01117">Descriptors.hpp:1117</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a484bafa2f8453a7c5a4a00b92a61b006"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">armnn::LstmInputParams::m_CellToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00048">LstmParams.hpp:48</a></div></div>
<div class="ttc" id="_quantized_lstm_params_8hpp_xhtml"><div class="ttname"><a href="_quantized_lstm_params_8hpp.xhtml">QuantizedLstmParams.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::BaseTensor::GetShape</a></div><div class="ttdeci">const TensorShape &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00169">Tensor.hpp:169</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a869254cb56968986a78a79e1d6d4a86b"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">armnn::ResizeDescriptor::m_Method</a></div><div class="ttdeci">ResizeMethod m_Method</div><div class="ttdoc">The Interpolation method to use (Bilinear, NearestNeighbor). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00749">Descriptors.hpp:749</a></div></div>
<div class="ttc" id="structarmnn_1_1_instance_normalization_descriptor_xhtml_a5e078fd505aef7bccaa05c8058e096cc"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a5e078fd505aef7bccaa05c8058e096cc">armnn::InstanceNormalizationDescriptor::m_Gamma</a></div><div class="ttdeci">float m_Gamma</div><div class="ttdoc">Gamma, the scale scalar value applied for the normalized tensor. Defaults to 1.0. ...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00647">Descriptors.hpp:647</a></div></div>
<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::SoftmaxDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Exponentiation value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00136">Descriptors.hpp:136</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a></div><div class="ttdoc">The padding fields don&amp;#39;t count and are ignored. </div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">armnn::BatchNormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Value to add to the variance. Used to avoid dividing by zero. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00623">Descriptors.hpp:623</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a9e081a9b94defb30d1558dc912507e0e"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">armnn::LstmInputParams::m_InputGateBias</a></div><div class="ttdeci">const ConstTensor * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00051">LstmParams.hpp:51</a></div></div>
<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml_ab1ae6f520bb1a4da191a0ae907477f23"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">armnn::ArgMinMaxDescriptor::m_Function</a></div><div class="ttdeci">ArgMinMaxFunction m_Function</div><div class="ttdoc">Specify if the function is to find Min or Max. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00056">Descriptors.hpp:56</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a7e2f87544b8bc7e497e1dec8d3ca4055"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7e2f87544b8bc7e497e1dec8d3ca4055">armnn::DetectionPostProcessDescriptor::m_DetectionsPerClass</a></div><div class="ttdeci">uint32_t m_DetectionsPerClass</div><div class="ttdoc">Detections per classes, used in Regular NMS. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00531">Descriptors.hpp:531</a></div></div>
<div class="ttc" id="_neon_end_to_end_tests_8cpp_xhtml_a358cb7cd3c0647b25be049fd734b8c22"><div class="ttname"><a href="_neon_end_to_end_tests_8cpp.xhtml#a358cb7cd3c0647b25be049fd734b8c22">anchorsInfo</a></div><div class="ttdeci">armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32)</div></div>
<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::BatchToSpaceNdDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00684">Descriptors.hpp:684</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a9e081a9b94defb30d1558dc912507e0e"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">armnn::QuantizedLstmInputParams::m_InputGateBias</a></div><div class="ttdeci">const ConstTensor * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00043">QuantizedLstmParams.hpp:43</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_network_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml">armnn::INetwork</a></div><div class="ttdoc">Main network class which provides the interface for building up a neural network. ...</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00105">INetwork.hpp:105</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::BaseTensor::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00175">Tensor.hpp:175</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a1759754ccb88ecc9af44f3aae6e244ee"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">armnn::LstmInputParams::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00046">LstmParams.hpp:46</a></div></div>
<div class="ttc" id="classarmnn_serializer_1_1_serializer_xhtml_a62dbb19d4776b489161b699f608f0150"><div class="ttname"><a href="classarmnn_serializer_1_1_serializer.xhtml#a62dbb19d4776b489161b699f608f0150">armnnSerializer::Serializer::Serialize</a></div><div class="ttdeci">void Serialize(const armnn::INetwork &amp;inNetwork) override</div><div class="ttdoc">Serializes the network to ArmNN SerializedGraph. </div><div class="ttdef"><b>Definition:</b> <a href="_serializer_8cpp_source.xhtml#l01564">Serializer.cpp:1564</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml_a405d5f966ec992d1717711e5a2d7909d"><div class="ttname"><a href="namespacearmnn_utils.xhtml#a405d5f966ec992d1717711e5a2d7909d">armnnUtils::Transpose</a></div><div class="ttdeci">void Transpose(const armnn::TensorShape &amp;dstShape, const armnn::PermutationVector &amp;mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="armnn_utils_2_transpose_8cpp_source.xhtml#l00120">Transpose.cpp:120</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ab023d9a7687e35c0f108458a094c1f56"><div class="ttname"><a href="namespacearmnn.xhtml#ab023d9a7687e35c0f108458a094c1f56">armnn::DepthToSpace</a></div><div class="ttdeci">void DepthToSpace(const TensorInfo &amp;inputInfo, const DepthToSpaceDescriptor &amp;descriptor, const void *inputData, void *outputData, unsigned int dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_depth_to_space_8cpp_source.xhtml#l00018">DepthToSpace.cpp:18</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00424">Descriptors.hpp:424</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00587">Descriptors.hpp:587</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_aa81f67ac64f0c249e26499600c45d996"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">armnn::BaseTensor::GetMemoryArea</a></div><div class="ttdeci">MemoryType GetMemoryArea() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00177">Tensor.hpp:177</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_af0f796fba1a2be9c56b4c9ee534577ee"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">armnn::LstmInputParams::m_ForgetLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_ForgetLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00058">LstmParams.hpp:58</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a35b112e30c3eb153806a2a8c16d178e3"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">armnn::LstmInputParams::m_CellToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00049">LstmParams.hpp:49</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a></div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00013">QuantizedLstmParams.hpp:13</a></div></div>
<div class="ttc" id="_lstm_params_8hpp_xhtml"><div class="ttname"><a href="_lstm_params_8hpp.xhtml">LstmParams.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::TransposeConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01111">Descriptors.hpp:1111</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::Convolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation along y axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00436">Descriptors.hpp:436</a></div></div>
<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_ac37e49c0d6e6e54f9d2015d0f11f8ee7"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">armnn::StridedSliceDescriptor::m_EndMask</a></div><div class="ttdeci">int32_t m_EndMask</div><div class="ttdoc">End mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01052">Descriptors.hpp:1052</a></div></div>
<div class="ttc" id="structarmnn_1_1_space_to_depth_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::SpaceToDepthDescriptor</a></div><div class="ttdoc">A SpaceToDepthDescriptor for the SpaceToDepthLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00810">Descriptors.hpp:810</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_reference_switch_xhtml_a77c7d528ac063d870b8c8426ec81c1c3"><div class="ttname"><a href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">armnn::OptionalReferenceSwitch::value</a></div><div class="ttdeci">const T &amp; value() const</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00146">Optional.hpp:146</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::DepthwiseConvolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation factor value for height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00488">Descriptors.hpp:488</a></div></div>
<div class="ttc" id="_serializer_tests_8cpp_xhtml_aa74276e373d72fd796aa827017af2196"><div class="ttname"><a href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a></div><div class="ttdeci">#define DECLARE_LAYER_VERIFIER_CLASS(name)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_tests_8cpp_source.xhtml#l00025">SerializerTests.cpp:25</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a></div><div class="ttdoc">A BatchToSpaceNdDescriptor for the BatchToSpaceNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00657">Descriptors.hpp:657</a></div></div>
<div class="ttc" id="_file_only_profiling_decorator_tests_8cpp_xhtml_a0c262ba6f6c189a2d092d127c1b7627b"><div class="ttname"><a href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a></div><div class="ttdeci">BOOST_CHECK(profilingService.GetCurrentState()==ProfilingState::WaitingForAck)</div></div>
<div class="ttc" id="namespacearmnn_xhtml_ab8cf8f9fb6792e654c2d8d8382f6f01b"><div class="ttname"><a href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a></div><div class="ttdeci">int LayerBindingId</div><div class="ttdoc">Type of identifiers for bindable layers (inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00171">Types.hpp:171</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a8c0f6d48705f40c5590dde09be262222"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">armnn::LstmInputParams::m_OutputGateBias</a></div><div class="ttdeci">const ConstTensor * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00054">LstmParams.hpp:54</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a6ef2dcac2ec0683d52df1b051404e7d6"><div class="ttname"><a href="namespacearmnn.xhtml#a6ef2dcac2ec0683d52df1b051404e7d6">armnn::Stack</a></div><div class="ttdeci">void Stack(const StackQueueDescriptor &amp;data, std::vector&lt; std::unique_ptr&lt; Decoder&lt; float &gt;&gt;&gt; &amp;inputs, Encoder&lt; float &gt; &amp;output)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_stack_8cpp_source.xhtml#l00012">Stack.cpp:12</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &amp;tensorInfo)=0</div></div>
<div class="ttc" id="namespacearmnn_xhtml_a81b5ff8545adad19a1c9d4ca076d552c"><div class="ttname"><a href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">armnn::GetDataTypeName</a></div><div class="ttdeci">constexpr const char * GetDataTypeName(DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00168">TypesUtils.hpp:168</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a></div><div class="ttdoc">A ResizeDescriptor for the ResizeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00724">Descriptors.hpp:724</a></div></div>
<div class="ttc" id="_serializer_tests_8cpp_xhtml_aafd0924b96830cf275f533b32ade856e"><div class="ttname"><a href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a></div><div class="ttdeci">BOOST_AUTO_TEST_CASE(SerializeAddition)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_tests_8cpp_source.xhtml#l00271">SerializerTests.cpp:271</a></div></div>
<div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::L2NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00606">Descriptors.hpp:606</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a9ae2c9796692ebeafe19a4d3f09c8ea8"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a9ae2c9796692ebeafe19a4d3f09c8ea8">armnn::DetectionPostProcessDescriptor::m_MaxClassesPerDetection</a></div><div class="ttdeci">uint32_t m_MaxClassesPerDetection</div><div class="ttdoc">Maximum numbers of classes per detection, used in Fast NMS. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00529">Descriptors.hpp:529</a></div></div>
<div class="ttc" id="structarmnn_1_1_mean_descriptor_xhtml_a1f0d67b087c491248bd1cde3ff995a95"><div class="ttname"><a href="structarmnn_1_1_mean_descriptor.xhtml#a1f0d67b087c491248bd1cde3ff995a95">armnn::MeanDescriptor::m_Axis</a></div><div class="ttdeci">std::vector&lt; unsigned int &gt; m_Axis</div><div class="ttdoc">Values for the dimensions to reduce. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00895">Descriptors.hpp:895</a></div></div>
<div class="ttc" id="structarmnn_1_1_stack_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.xhtml">armnn::StackDescriptor</a></div><div class="ttdoc">A StackDescriptor for the StackLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00950">Descriptors.hpp:950</a></div></div>
<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::SpaceToBatchNdDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00806">Descriptors.hpp:806</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_affcee5f4ab5994a21bee3b78b4e43de3"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">armnn::QuantizedLstmInputParams::m_InputToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00033">QuantizedLstmParams.hpp:33</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a28e115f5d28500324b53fae9e6c00b77"><div class="ttname"><a href="namespacearmnn.xhtml#a28e115f5d28500324b53fae9e6c00b77">armnn::Pad</a></div><div class="ttdeci">void Pad(const TensorInfo &amp;inputInfo, const TensorInfo &amp;outputInfo, std::vector&lt; std::pair&lt; unsigned int, unsigned int &gt;&gt; m_padList, const T *inputData, T *outData, const float padValue)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_pad_8cpp_source.xhtml#l00022">Pad.cpp:22</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00426">Descriptors.hpp:426</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ae72089bcab60ac175557f4241b16a014"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">armnn::DetectionPostProcessDescriptor::m_MaxDetections</a></div><div class="ttdeci">uint32_t m_MaxDetections</div><div class="ttdoc">Maximum numbers of detections. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00527">Descriptors.hpp:527</a></div></div>
<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a></div><div class="ttdoc">A PadDescriptor for the PadLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00901">Descriptors.hpp:901</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &amp;dstShape, const armnn::PermutationVector &amp;mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00121">Permute.cpp:121</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00430">Descriptors.hpp:430</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a0cd848f65ec31778d708852f0042fe37"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">armnn::LstmInputParams::m_InputLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_InputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00057">LstmParams.hpp:57</a></div></div>
<div class="ttc" id="_types_utils_8hpp_xhtml"><div class="ttname"><a href="_types_utils_8hpp.xhtml">TypesUtils.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00482">Descriptors.hpp:482</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_detection_post_process_descriptor_xhtml_a53c8a7f33a40e1e240256bcfcf41b101"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">armnn::DetectionPostProcessDescriptor::m_NmsIouThreshold</a></div><div class="ttdeci">float m_NmsIouThreshold</div><div class="ttdoc">Intersection over union threshold. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00535">Descriptors.hpp:535</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a56b81ca8ba4b4937e0787e4951f043fc"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">armnn::LstmInputParams::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00047">LstmParams.hpp:47</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a></div><div class="ttdoc">An LstmDescriptor for the LstmLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00837">Descriptors.hpp:837</a></div></div>
<div class="ttc" id="_deprecated_8hpp_xhtml_ad762b11b48e5c1d1c1743f529485728a"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_END</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00034">Deprecated.hpp:34</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::DepthwiseConvolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation factor value for width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00486">Descriptors.hpp:486</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00478">Descriptors.hpp:478</a></div></div>
<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_aae0893695f5803a3517985c7cb1ccb2e"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">armnn::ViewsDescriptor::SetViewSize</a></div><div class="ttdeci">Status SetViewSize(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the size of the views. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00307">Descriptors.cpp:307</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::ArgMinMaxFunction::Max</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a></div><div class="ttdoc">An output connection slot for a layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00037">INetwork.hpp:37</a></div></div>
<div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml">armnn::L2NormalizationDescriptor</a></div><div class="ttdoc">A L2NormalizationDescriptor for the L2NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00591">Descriptors.hpp:591</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a44b0e6b16708df7f0d2bbab141688aaa"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">armnn::LstmInputParams::m_ProjectionBias</a></div><div class="ttdeci">const ConstTensor * m_ProjectionBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00056">LstmParams.hpp:56</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00264">Tensor.cpp:264</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a31da1ead6794dd64571afdd0b6efc771"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">armnn::QuantizedLstmInputParams::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00034">QuantizedLstmParams.hpp:34</a></div></div>
<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml">armnn::ArgMinMaxDescriptor</a></div><div class="ttdoc">An ArgMinMaxDescriptor for ArgMinMaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00043">Descriptors.hpp:43</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00247">Tensor.cpp:247</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00095">Tensor.hpp:95</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00147">Descriptors.hpp:147</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="classarmnn_1_1_optional_base_xhtml_a86b749ce2c4bc627fa8a1fcfaf0e314f"><div class="ttname"><a href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">armnn::OptionalBase::has_value</a></div><div class="ttdeci">bool has_value() const noexcept</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00053">Optional.hpp:53</a></div></div>
<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a></div><div class="ttdoc">A FullyConnectedDescriptor for the FullyConnectedLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00373">Descriptors.hpp:373</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#l00386">Descriptors.hpp:386</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00199">Tensor.hpp:199</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a></div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00013">LstmParams.hpp:13</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a8c0f6d48705f40c5590dde09be262222"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">armnn::QuantizedLstmInputParams::m_OutputGateBias</a></div><div class="ttdeci">const ConstTensor * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00046">QuantizedLstmParams.hpp:46</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_adcf5037208faac36c0788239a073f75c"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">armnn::ResizeDescriptor::m_TargetWidth</a></div><div class="ttdeci">uint32_t m_TargetWidth</div><div class="ttdoc">Target width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00744">Descriptors.hpp:744</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">armnn::LstmDescriptor::m_PeepholeEnabled</a></div><div class="ttdeci">bool m_PeepholeEnabled</div><div class="ttdoc">Enable/disable peephole. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00869">Descriptors.hpp:869</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a3a04b0ccee4bb2f21721ee5045e83df4"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a3a04b0ccee4bb2f21721ee5045e83df4">armnn::DetectionPostProcessDescriptor::m_NumClasses</a></div><div class="ttdeci">uint32_t m_NumClasses</div><div class="ttdoc">Number of classes. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00537">Descriptors.hpp:537</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58ca19bb0af2c3c530538cb41aff7f235b96"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca19bb0af2c3c530538cb41aff7f235b96">armnn::ComparisonOperation::NotEqual</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::TransposeConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01109">Descriptors.hpp:1109</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00259">Tensor.cpp:259</a></div></div>
<div class="ttc" id="structarmnn_1_1_stand_in_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_stand_in_descriptor.xhtml">armnn::StandInDescriptor</a></div><div class="ttdoc">A StandInDescriptor for the StandIn layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00980">Descriptors.hpp:980</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad773a034fb9983e15f3094b4c5c7c30c"><div class="ttname"><a href="namespacearmnn.xhtml#ad773a034fb9983e15f3094b4c5c7c30c">armnn::Quantize</a></div><div class="ttdeci">QuantizedType Quantize(float value, float scale, int32_t offset)</div><div class="ttdoc">Quantize a floating point data type into an 8-bit data type. </div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8cpp_source.xhtml#l00031">TypesUtils.cpp:31</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a7ed9bc7c26df67d274d5dd4cd83adf0f"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7ed9bc7c26df67d274d5dd4cd83adf0f">armnn::DetectionPostProcessDescriptor::m_UseRegularNms</a></div><div class="ttdeci">bool m_UseRegularNms</div><div class="ttdoc">Use Regular NMS. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00539">Descriptors.hpp:539</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::TransposeConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01119">Descriptors.hpp:1119</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a25dc224be48103343302b5a6fd588fe7"><div class="ttname"><a href="namespacearmnn.xhtml#a25dc224be48103343302b5a6fd588fe7">armnn::Resize</a></div><div class="ttdeci">void Resize(Decoder&lt; float &gt; &amp;in, const TensorInfo &amp;inputInfo, Encoder&lt; float &gt; &amp;out, const TensorInfo &amp;outputInfo, DataLayoutIndexed dataLayout, armnn::ResizeMethod resizeMethod, bool alignCorners)</div><div class="ttdef"><b>Definition:</b> <a href="_resize_8cpp_source.xhtml#l00035">Resize.cpp:35</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ac52e04c0e349e25bcdaa72c27395ef8f"><div class="ttname"><a href="namespacearmnn.xhtml#ac52e04c0e349e25bcdaa72c27395ef8f">armnn::LogSoftmax</a></div><div class="ttdeci">void LogSoftmax(Decoder&lt; float &gt; &amp;input, Encoder&lt; float &gt; &amp;output, const TensorInfo &amp;inputInfo, const LogSoftmaxDescriptor &amp;descriptor)</div><div class="ttdef"><b>Definition:</b> <a href="_log_softmax_8cpp_source.xhtml#l00030">LogSoftmax.cpp:30</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8aeddebdcf02e1832b22203c08a6b678"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">armnn::BaseTensor::GetInfo</a></div><div class="ttdeci">const TensorInfo &amp; GetInfo() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00167">Tensor.hpp:167</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a46c3fa15c46fb0d1dcdc24d0ea5cb5cd"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">armnn::ResizeDescriptor::m_TargetHeight</a></div><div class="ttdeci">uint32_t m_TargetHeight</div><div class="ttdoc">Target height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00746">Descriptors.hpp:746</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ae1b07ed928036004bd257169e5aeeef4"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">armnn::LstmDescriptor::m_ActivationFunc</a></div><div class="ttdeci">uint32_t m_ActivationFunc</div><div class="ttdoc">The activation function to use. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00861">Descriptors.hpp:861</a></div></div>
<div class="ttc" id="structarmnn_1_1_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_slice_descriptor.xhtml">armnn::SliceDescriptor</a></div><div class="ttdoc">A SliceDescriptor for the SliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00927">Descriptors.hpp:927</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_visitor_base_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer_visitor_base.xhtml">armnn::LayerVisitorBase</a></div><div class="ttdoc">Visitor base class with empty implementations. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_visitor_base_8hpp_source.xhtml#l00025">LayerVisitorBase.hpp:25</a></div></div>
<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00173">Types.hpp:173</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00432">Descriptors.hpp:432</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4a180e425d4c19b2cdea4ce5760180e1"><div class="ttname"><a href="namespacearmnn.xhtml#a4a180e425d4c19b2cdea4ce5760180e1">armnn::SpaceToBatchNd</a></div><div class="ttdeci">void SpaceToBatchNd(const TensorInfo &amp;inputInfo, const TensorInfo &amp;outputInfo, const SpaceToBatchNdDescriptor &amp;params, Decoder&lt; float &gt; &amp;inputData, Encoder&lt; float &gt; &amp;outputData)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_space_to_batch_n_d_8cpp_source.xhtml#l00034">SpaceToBatchNd.cpp:34</a></div></div>
<div class="ttc" id="classarmnn_serializer_1_1_serializer_xhtml_af21f36069c661c4afa7221a305de80e0"><div class="ttname"><a href="classarmnn_serializer_1_1_serializer.xhtml#af21f36069c661c4afa7221a305de80e0">armnnSerializer::Serializer::SaveSerializedToStream</a></div><div class="ttdeci">bool SaveSerializedToStream(std::ostream &amp;stream) override</div><div class="ttdoc">Serializes the SerializedGraph to the stream. </div><div class="ttdef"><b>Definition:</b> <a href="_serializer_8cpp_source.xhtml#l01582">Serializer.cpp:1582</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a435d3651482bbfcc11263b4e4e0c900f"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">armnn::QuantizedLstmInputParams::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00038">QuantizedLstmParams.hpp:38</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a531a3907ec13d3772370da88030191a5"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">armnn::LstmDescriptor::m_ClippingThresCell</a></div><div class="ttdeci">float m_ClippingThresCell</div><div class="ttdoc">Clipping threshold value for the cell state. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00863">Descriptors.hpp:863</a></div></div>
<div class="ttc" id="structarmnn_1_1_space_to_depth_descriptor_xhtml_a6c6b8957f1e176867e5fb05b1a1a1486"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_descriptor.xhtml#a6c6b8957f1e176867e5fb05b1a1a1486">armnn::SpaceToDepthDescriptor::m_BlockSize</a></div><div class="ttdeci">unsigned int m_BlockSize</div><div class="ttdoc">Scalar specifying the input block size. It must be &gt;= 1. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00827">Descriptors.hpp:827</a></div></div>
<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Pooling2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00369">Descriptors.hpp:369</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_aa61510cbd529870182e918ac6e8b9d72"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#aa61510cbd529870182e918ac6e8b9d72">armnn::DetectionPostProcessDescriptor::m_ScaleH</a></div><div class="ttdeci">float m_ScaleH</div><div class="ttdoc">Center size encoding scale height. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00547">Descriptors.hpp:547</a></div></div>
<div class="ttc" id="structarmnn_1_1_comparison_descriptor_xhtml_a865dc4f43cb0ff01a1dcf78036912fd1"><div class="ttname"><a href="structarmnn_1_1_comparison_descriptor.xhtml#a865dc4f43cb0ff01a1dcf78036912fd1">armnn::ComparisonDescriptor::m_Operation</a></div><div class="ttdeci">ComparisonOperation m_Operation</div><div class="ttdoc">Specifies the comparison operation to execute. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00078">Descriptors.hpp:78</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ad0b8c32bb5381f4cc999093ba3b98b43"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">armnn::LstmInputParams::m_CellLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_CellLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00059">LstmParams.hpp:59</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ace7a1f1f1041b412b7d8ef82b95ff352"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">armnn::LstmInputParams::m_ForgetGateBias</a></div><div class="ttdeci">const ConstTensor * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00052">LstmParams.hpp:52</a></div></div>
<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">armnn::SpaceToBatchNdDescriptor</a></div><div class="ttdoc">A SpaceToBatchNdDescriptor for the SpaceToBatchNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00778">Descriptors.hpp:778</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a49e11acda22742cbaf6f1b259ead0d84"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">armnn::LstmInputParams::m_InputToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00042">LstmParams.hpp:42</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a6e30c7b3451da3ea9cf4259fb602e6e6"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">armnn::LstmInputParams::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00043">LstmParams.hpp:43</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::Convolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation along x axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00434">Descriptors.hpp:434</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="structarmnn_1_1_lstm_descriptor_xhtml_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::LstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable cifg (coupled input &amp; forget gate). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00867">Descriptors.hpp:867</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::TransposeConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01105">Descriptors.hpp:1105</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a86d7a7168ac00b75b4971f9aad623698"><div class="ttname"><a href="namespacearmnn.xhtml#a86d7a7168ac00b75b4971f9aad623698">armnn::StridedSlice</a></div><div class="ttdeci">void StridedSlice(const TensorInfo &amp;inputInfo, const StridedSliceDescriptor &amp;params, const void *inputData, void *outputData, unsigned int dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_strided_slice_8cpp_source.xhtml#l00090">StridedSlice.cpp:90</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::TransposeConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01113">Descriptors.hpp:1113</a></div></div>
<div class="ttc" id="_i_deserializer_8hpp_xhtml"><div class="ttname"><a href="_i_deserializer_8hpp.xhtml">IDeserializer.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ae83131e16df1cace69395a5f99bc5ecb"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">armnn::LstmInputParams::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00045">LstmParams.hpp:45</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00484">Descriptors.hpp:484</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::TransposeConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01115">Descriptors.hpp:1115</a></div></div>
<div class="ttc" id="classarmnn_1_1_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_exception.xhtml">armnn::Exception</a></div><div class="ttdoc">Base class for all ArmNN exceptions so that users can filter to just those. </div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00046">Exceptions.hpp:46</a></div></div>
<div class="ttc" id="_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f"><div class="ttname"><a href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::ResizeMethod::Bilinear</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a1759754ccb88ecc9af44f3aae6e244ee"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">armnn::QuantizedLstmInputParams::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00040">QuantizedLstmParams.hpp:40</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot &amp; GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a435d3651482bbfcc11263b4e4e0c900f"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">armnn::LstmInputParams::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00044">LstmParams.hpp:44</a></div></div>
<div class="ttc" id="structarmnn_1_1_mean_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mean_descriptor.xhtml">armnn::MeanDescriptor</a></div><div class="ttdoc">A MeanDescriptor for the MeanLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00877">Descriptors.hpp:877</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a6e30c7b3451da3ea9cf4259fb602e6e6"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">armnn::QuantizedLstmInputParams::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00036">QuantizedLstmParams.hpp:36</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a165ae372a7f67cad64ef3395d30122ce"><div class="ttname"><a href="namespacearmnn.xhtml#a165ae372a7f67cad64ef3395d30122ce">armnn::Mean</a></div><div class="ttdeci">void Mean(const armnn::TensorInfo &amp;inputInfo, const armnn::TensorInfo &amp;outputInfo, const std::vector&lt; unsigned int &gt; &amp;axis, Decoder&lt; float &gt; &amp;input, Encoder&lt; float &gt; &amp;output)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_mean_8cpp_source.xhtml#l00071">Mean.cpp:71</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_input_slot_xhtml_a81fbf6103761e55061b62ba989b00f10"><div class="ttname"><a href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">armnn::IInputSlot::GetConnection</a></div><div class="ttdeci">virtual const IOutputSlot * GetConnection() const =0</div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">armnn::LstmDescriptor::m_LayerNormEnabled</a></div><div class="ttdeci">bool m_LayerNormEnabled</div><div class="ttdoc">Enable/disable layer normalization. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00873">Descriptors.hpp:873</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::TransposeConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01107">Descriptors.hpp:1107</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_descriptor.xhtml">armnn::TransposeDescriptor</a></div><div class="ttdoc">A TransposeDescriptor for the TransposeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01123">Descriptors.hpp:1123</a></div></div>
<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml">armnn::StridedSliceDescriptor</a></div><div class="ttdoc">A StridedSliceDescriptor for the StridedSliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01002">Descriptors.hpp:1002</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="classarmnn_1_1_i_output_slot_xhtml_a9943775a364fc4ab53b85ac88f311886"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">armnn::IOutputSlot::GetTensorInfo</a></div><div class="ttdeci">virtual const TensorInfo &amp; GetTensorInfo() const =0</div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot &amp; GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_a9a2af2f8c4af4f9efa8e79417d505ac4aaa020331bb30d2fa2ecf7c3a0777823f"><div class="ttname"><a href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaa020331bb30d2fa2ecf7c3a0777823f">armnn::ResizeMethod::NearestNeighbor</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a5e1dc69443b64ad16b669388a6023f7a"><div class="ttname"><a href="namespacearmnn.xhtml#a5e1dc69443b64ad16b669388a6023f7a">armnn::SpaceToDepth</a></div><div class="ttdeci">void SpaceToDepth(const TensorInfo &amp;inputInfo, const TensorInfo &amp;outputInfo, const SpaceToDepthDescriptor &amp;params, Decoder&lt; float &gt; &amp;inputData, Encoder&lt; float &gt; &amp;outputData)</div><div class="ttdef"><b>Definition:</b> <a href="_space_to_depth_8cpp_source.xhtml#l00036">SpaceToDepth.cpp:36</a></div></div>
<div class="ttc" id="classarmnn_serializer_1_1_serializer_xhtml"><div class="ttname"><a href="classarmnn_serializer_1_1_serializer.xhtml">armnnSerializer::Serializer</a></div><div class="ttdef"><b>Definition:</b> <a href="_serializer_8hpp_source.xhtml#l00324">Serializer.hpp:324</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a7a2156ec7d9c012ce00bbcc6afcb9028"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7a2156ec7d9c012ce00bbcc6afcb9028">armnn::DetectionPostProcessDescriptor::m_ScaleY</a></div><div class="ttdeci">float m_ScaleY</div><div class="ttdoc">Center size encoding scale y. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00543">Descriptors.hpp:543</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a733ae6b70d0bfa43433c3e7606992328"><div class="ttname"><a href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a></div><div class="ttdeci">OriginsDescriptor CreateDescriptorForConcatenation(TensorShapeIt first, TensorShapeIt last, unsigned int concatenationDimension)</div><div class="ttdoc">Convenience template to create an OriginsDescriptor to use when creating a ConcatLayer for performing...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00242">Descriptors.hpp:242</a></div></div>
<div class="ttc" id="_serializer_tests_8cpp_xhtml_a664bc60b972af17162b98d384ea031c1"><div class="ttname"><a href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a></div><div class="ttdeci">#define DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(name)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_tests_8cpp_source.xhtml#l00040">SerializerTests.cpp:40</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a4392dd6b4862cc9cf95ae8f1001ba592"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a4392dd6b4862cc9cf95ae8f1001ba592">armnn::DetectionPostProcessDescriptor::m_NmsScoreThreshold</a></div><div class="ttdeci">float m_NmsScoreThreshold</div><div class="ttdoc">NMS score threshold. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00533">Descriptors.hpp:533</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr&lt; INetwork, void(*)(INetwork *network)&gt; INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00101">INetwork.hpp:101</a></div></div>
<div class="ttc" id="classarmnn_deserializer_1_1_i_deserializer_xhtml"><div class="ttname"><a href="classarmnn_deserializer_1_1_i_deserializer.xhtml">armnnDeserializer::IDeserializer</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_deserializer_8hpp_source.xhtml#l00027">IDeserializer.hpp:27</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a8746512fab5ec10c2c57800c66311ba7"><div class="ttname"><a href="namespacearmnn.xhtml#a8746512fab5ec10c2c57800c66311ba7">armnn::BatchToSpaceNd</a></div><div class="ttdeci">void BatchToSpaceNd(const DataLayoutIndexed &amp;dataLayout, const TensorInfo &amp;inputTensorInfo, const TensorInfo &amp;outputTensorInfo, const std::vector&lt; unsigned int &gt; &amp;blockShape, const std::vector&lt; std::pair&lt; unsigned int, unsigned int &gt;&gt; &amp;cropsData, Decoder&lt; float &gt; &amp;inputDecoder, Encoder&lt; float &gt; &amp;outputEncoder)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_batch_to_space_n_d_8cpp_source.xhtml#l00035">BatchToSpaceNd.cpp:35</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &amp;destination)=0</div></div>
<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#aea909c7327109228ef618d459015def3">armnn::BaseTensor::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00172">Tensor.hpp:172</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00313">Descriptors.hpp:313</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a9b18daea2e9f42386055326fd016519a"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">armnn::LstmInputParams::m_OutputLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_OutputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00060">LstmParams.hpp:60</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a></div><div class="ttdoc">A NormalizationDescriptor for the NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00551">Descriptors.hpp:551</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2e93e304cf516841c521e3eaee025cd"><div class="ttname"><a href="namespacearmnn.xhtml#ae2e93e304cf516841c521e3eaee025cd">armnn::Pooling2d</a></div><div class="ttdeci">void Pooling2d(Decoder&lt; float &gt; &amp;rInputDecoder, Encoder&lt; float &gt; &amp;rOutputEncoder, const TensorInfo &amp;inputInfo, const TensorInfo &amp;outputInfo, const Pooling2dDescriptor &amp;params)</div><div class="ttdoc">Computes the Pooling2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_pooling2d_8cpp_source.xhtml#l00143">Pooling2d.cpp:143</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::ResizeDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00751">Descriptors.hpp:751</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml">armnn::DetectionPostProcessDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00495">Descriptors.hpp:495</a></div></div>
<div class="ttc" id="structarmnn_1_1_instance_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_descriptor.xhtml">armnn::InstanceNormalizationDescriptor</a></div><div class="ttdoc">An InstanceNormalizationDescriptor for InstanceNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00629">Descriptors.hpp:629</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_bilinear_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_resize_bilinear_descriptor.xhtml">armnn::ResizeBilinearDescriptor</a></div><div class="ttdoc">A ResizeBilinearDescriptor for the ResizeBilinearLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00707">Descriptors.hpp:707</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a427c3d26d05b518b1ace407035f5920e"><div class="ttname"><a href="namespacearmnn.xhtml#a427c3d26d05b518b1ace407035f5920e">armnn::Splitter</a></div><div class="ttdeci">void Splitter(const SplitterQueueDescriptor &amp;data)</div><div class="ttdef"><b>Definition:</b> <a href="_splitter_8hpp_source.xhtml#l00017">Splitter.hpp:17</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa999ff2585ad75b95954a9323f63c32b"><div class="ttname"><a href="namespacearmnn.xhtml#aa999ff2585ad75b95954a9323f63c32b">armnn::Softmax</a></div><div class="ttdeci">void Softmax(Decoder&lt; float &gt; &amp;in, Encoder&lt; float &gt; &amp;out, const TensorInfo &amp;inputTensorInfo, float beta, int axis)</div><div class="ttdoc">Computes the softmax function on some inputs, into outputs, with a shape given by tensorInfo...</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_softmax_8cpp_source.xhtml#l00017">Softmax.cpp:17</a></div></div>
<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a></div><div class="ttdoc">A SoftmaxDescriptor for the SoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00123">Descriptors.hpp:123</a></div></div>
<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_a2b125117aa61f9baf3a9cb8658aa61a2"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">armnn::ViewsDescriptor::SetViewOriginCoord</a></div><div class="ttdeci">Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the view origin coordinates. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00302">Descriptors.cpp:302</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a706f7345af3f18f4b16e226a672214c6"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create()</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00049">Network.cpp:49</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00444">Descriptors.hpp:444</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a></div><div class="ttdoc">A BatchNormalizationDescriptor for the BatchNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00610">Descriptors.hpp:610</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00422">Descriptors.hpp:422</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_abcbdfb544ece4c31d0b37715ad0f3be0"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">armnn::BaseTensor::GetNumBytes</a></div><div class="ttdeci">unsigned int GetNumBytes() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00174">Tensor.hpp:174</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a31da1ead6794dd64571afdd0b6efc771"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">armnn::LstmInputParams::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00041">LstmParams.hpp:41</a></div></div>
<div class="ttc" id="structarmnn_1_1_permute_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_permute_descriptor.xhtml">armnn::PermuteDescriptor</a></div><div class="ttdoc">A PermuteDescriptor for the PermuteLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00102">Descriptors.hpp:102</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00476">Descriptors.hpp:476</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_affcee5f4ab5994a21bee3b78b4e43de3"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">armnn::LstmInputParams::m_InputToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00040">LstmParams.hpp:40</a></div></div>
<div class="ttc" id="_neon_end_to_end_tests_8cpp_xhtml_ac0981848e4ae57729f14f72bd4caa9f8"><div class="ttname"><a href="_neon_end_to_end_tests_8cpp.xhtml#ac0981848e4ae57729f14f72bd4caa9f8">anchors</a></div><div class="ttdeci">std::vector&lt; float &gt; anchors({ 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 10.5f, 1.0f, 1.0f, 0.5f, 10.5f, 1.0f, 1.0f, 0.5f, 100.5f, 1.0f, 1.0f })</div></div>
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