21.11/armnn_tf_lite_parser_2test_2_conv2_d_8cpp.xhtml


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<div class="textblock"><code>#include &quot;<a class="el" href="_parser_flatbuffers_fixture_8hpp_source.xhtml">ParserFlatbuffersFixture.hpp</a>&quot;</code><br />
<code>#include &lt;sstream&gt;</code><br />
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<h2 class="memtitle"><span class="permalink"><a href="#a1b366359ea636bfa92fa5ea0694a3ca3">&#9670;&nbsp;</a></span>TEST_SUITE()</h2>

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          <td class="memname">TEST_SUITE </td>
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<p class="definition">Definition at line <a class="el" href="armnn_tf_lite_parser_2test_2_conv2_d_8cpp_source.xhtml#l00009">9</a> of file <a class="el" href="armnn_tf_lite_parser_2test_2_conv2_d_8cpp_source.xhtml">Conv2D.cpp</a>.</p>

<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::QAsymmU8</a>, <a class="el" href="_parser_flatbuffers_fixture_8hpp_source.xhtml#l00153">ParserFlatbuffersFixture::SetupSingleInputSingleOutput()</a>, and <a class="el" href="_mem_copy_tests_8cpp_source.xhtml#l00045">TEST_CASE_FIXTURE()</a>.</p>
<div class="fragment"><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;{</div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="keyword">struct </span>SimpleConv2DFixture : <span class="keyword">public</span> <a class="code" href="struct_parser_flatbuffers_fixture.xhtml">ParserFlatbuffersFixture</a></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;{</div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;    <span class="keyword">explicit</span> SimpleConv2DFixture()</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;        m_JsonString = R<span class="stringliteral">&quot;(</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="stringliteral">            {</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="stringliteral">                &quot;version&quot;: 3,</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="stringliteral">                &quot;operator_codes&quot;: [ { &quot;builtin_code&quot;: &quot;CONV_2D&quot; } ],</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="stringliteral">                &quot;subgraphs&quot;: [ {</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="stringliteral">                    &quot;tensors&quot;: [</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="stringliteral">                        {</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="stringliteral">                            &quot;shape&quot;: [ 1, 3, 3, 1 ],</span></div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="stringliteral">                            &quot;type&quot;: &quot;UINT8&quot;,</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="stringliteral">                            &quot;buffer&quot;: 0,</span></div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;<span class="stringliteral">                            &quot;name&quot;: &quot;inputTensor&quot;,</span></div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="stringliteral">                            &quot;quantization&quot;: {</span></div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="stringliteral">                                &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="stringliteral">                                &quot;max&quot;: [ 255.0 ],</span></div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;<span class="stringliteral">                                &quot;scale&quot;: [ 1.0 ],</span></div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="stringliteral">                                &quot;zero_point&quot;: [ 0 ],</span></div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="stringliteral">                            }</span></div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="stringliteral">                        },</span></div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="stringliteral">                        {</span></div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="stringliteral">                            &quot;shape&quot;: [ 1, 1, 1, 1 ],</span></div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;<span class="stringliteral">                            &quot;type&quot;: &quot;UINT8&quot;,</span></div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="stringliteral">                            &quot;buffer&quot;: 1,</span></div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="stringliteral">                            &quot;name&quot;: &quot;outputTensor&quot;,</span></div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="stringliteral">                            &quot;quantization&quot;: {</span></div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="stringliteral">                                &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="stringliteral">                                &quot;max&quot;: [ 511.0 ],</span></div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="stringliteral">                                &quot;scale&quot;: [ 2.0 ],</span></div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="stringliteral">                                &quot;zero_point&quot;: [ 0 ],</span></div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="stringliteral">                            }</span></div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="stringliteral">                        },</span></div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="stringliteral">                        {</span></div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="stringliteral">                            &quot;shape&quot;: [ 1, 3, 3, 1 ],</span></div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="stringliteral">                            &quot;type&quot;: &quot;UINT8&quot;,</span></div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="stringliteral">                            &quot;buffer&quot;: 2,</span></div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="stringliteral">                            &quot;name&quot;: &quot;filterTensor&quot;,</span></div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="stringliteral">                            &quot;quantization&quot;: {</span></div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;<span class="stringliteral">                                &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;<span class="stringliteral">                                &quot;max&quot;: [ 255.0 ],</span></div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;<span class="stringliteral">                                &quot;scale&quot;: [ 1.0 ],</span></div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;<span class="stringliteral">                                &quot;zero_point&quot;: [ 0 ],</span></div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;<span class="stringliteral">                            }</span></div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;<span class="stringliteral">                        }</span></div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;<span class="stringliteral">                    ],</span></div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;<span class="stringliteral">                    &quot;inputs&quot;: [ 0 ],</span></div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;<span class="stringliteral">                    &quot;outputs&quot;: [ 1 ],</span></div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;<span class="stringliteral">                    &quot;operators&quot;: [</span></div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;<span class="stringliteral">                        {</span></div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;<span class="stringliteral">                            &quot;opcode_index&quot;: 0,</span></div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;<span class="stringliteral">                            &quot;inputs&quot;: [ 0, 2 ],</span></div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;<span class="stringliteral">                            &quot;outputs&quot;: [ 1 ],</span></div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;<span class="stringliteral">                            &quot;builtin_options_type&quot;: &quot;Conv2DOptions&quot;,</span></div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;<span class="stringliteral">                            &quot;builtin_options&quot;: {</span></div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;<span class="stringliteral">                                &quot;padding&quot;: &quot;VALID&quot;,</span></div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;<span class="stringliteral">                                &quot;stride_w&quot;: 1,</span></div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;<span class="stringliteral">                                &quot;stride_h&quot;: 1,</span></div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;<span class="stringliteral">                                &quot;fused_activation_function&quot;: &quot;NONE&quot;</span></div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;<span class="stringliteral">                            },</span></div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;<span class="stringliteral">                            &quot;custom_options_format&quot;: &quot;FLEXBUFFERS&quot;</span></div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;<span class="stringliteral">                        }</span></div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;<span class="stringliteral">                    ],</span></div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;<span class="stringliteral">                } ],</span></div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;<span class="stringliteral">                &quot;buffers&quot; : [</span></div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;<span class="stringliteral">                    { },</span></div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;<span class="stringliteral">                    { },</span></div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;<span class="stringliteral">                    { &quot;data&quot;: [ 2,1,0,  6,2,1, 4,1,2 ], },</span></div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;<span class="stringliteral">                    { },</span></div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;<span class="stringliteral">                ]</span></div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;<span class="stringliteral">            }</span></div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;<span class="stringliteral">        )&quot;;</span></div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;<span class="stringliteral">        <a class="code" href="struct_parser_flatbuffers_fixture.xhtml#a2bb4ea256fbbf6d53068ca93bb4bc95c">SetupSingleInputSingleOutput</a>(</span><span class="stringliteral">&quot;inputTensor&quot;</span>, <span class="stringliteral">&quot;outputTensor&quot;</span>);</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;};</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;<a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(SimpleConv2DFixture, <span class="stringliteral">&quot;ParseSimpleConv2D&quot;</span>)</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;{</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    RunTest&lt;4, armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        0,</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;            1, 2, 3,</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;            4, 5, 6,</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;            7, 8, 9,</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        },</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;        <span class="comment">// because of the output scaling we need to take half of the values</span></div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;        {</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;            (1*2 + 2*1 + 3*0 +</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;             4*6 + 5*2 + 6*1 +</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;             7*4 + 8*1 + 9*2) /2</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;        });</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;}</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="keyword">struct </span>Conv2DWithBiasesFixture : <span class="keyword">public</span> <a class="code" href="struct_parser_flatbuffers_fixture.xhtml">ParserFlatbuffersFixture</a></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">explicit</span> Conv2DWithBiasesFixture(<span class="keyword">const</span> std::string &amp; inputShape,</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;                                     <span class="keyword">const</span> std::string &amp; outputShape,</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;                                     <span class="keyword">const</span> std::string &amp; filterShape,</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;                                     <span class="keyword">const</span> std::string &amp; filterData,</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;                                     <span class="keyword">const</span> std::string &amp; biasShape,</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;                                     <span class="keyword">const</span> std::string &amp; biasData,</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;                                     <span class="keyword">const</span> std::string &amp; strides,</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;                                     <span class="keyword">const</span> std::string &amp; activation=<span class="stringliteral">&quot;NONE&quot;</span>,</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;                                     <span class="keyword">const</span> std::string &amp; filterScale=<span class="stringliteral">&quot;1.0&quot;</span>,</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;                                     <span class="keyword">const</span> std::string &amp; filterZeroPoint=<span class="stringliteral">&quot;0&quot;</span>,</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;                                     <span class="keyword">const</span> std::string &amp; outputScale=<span class="stringliteral">&quot;2.0&quot;</span>,</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;                                     <span class="keyword">const</span> std::string &amp; outputZeroPoint=<span class="stringliteral">&quot;0&quot;</span>)</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    {</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;        m_JsonString = R<span class="stringliteral">&quot;(</span></div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;<span class="stringliteral">            {</span></div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;<span class="stringliteral">                &quot;version&quot;: 3,</span></div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;<span class="stringliteral">                &quot;operator_codes&quot;: [ { &quot;builtin_code&quot;: &quot;CONV_2D&quot; } ],</span></div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;<span class="stringliteral">                &quot;subgraphs&quot;: [ {</span></div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;<span class="stringliteral">                    &quot;tensors&quot;: [</span></div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;<span class="stringliteral">                        {</span></div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;<span class="stringliteral">                            &quot;shape&quot;: )&quot; + inputShape + R</span><span class="stringliteral">&quot;(,</span></div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;<span class="stringliteral">                            &quot;type&quot;: &quot;UINT8&quot;,</span></div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;<span class="stringliteral">                            &quot;buffer&quot;: 0,</span></div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;<span class="stringliteral">                            &quot;name&quot;: &quot;inputTensor&quot;,</span></div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;<span class="stringliteral">                            &quot;quantization&quot;: {</span></div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;<span class="stringliteral">                                &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;<span class="stringliteral">                                &quot;max&quot;: [ 255.0 ],</span></div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;<span class="stringliteral">                                &quot;scale&quot;: [ 1.0 ],</span></div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;<span class="stringliteral">                                &quot;zero_point&quot;: [ 0 ],</span></div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;<span class="stringliteral">                            }</span></div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;<span class="stringliteral">                        },</span></div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;<span class="stringliteral">                        {</span></div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;<span class="stringliteral">                            &quot;shape&quot;: )&quot; + outputShape + R</span><span class="stringliteral">&quot;(,</span></div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;<span class="stringliteral">                            &quot;type&quot;: &quot;UINT8&quot;,</span></div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;<span class="stringliteral">                            &quot;buffer&quot;: 1,</span></div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;<span class="stringliteral">                            &quot;name&quot;: &quot;outputTensor&quot;,</span></div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;<span class="stringliteral">                            &quot;quantization&quot;: {</span></div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;<span class="stringliteral">                                &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;<span class="stringliteral">                                &quot;max&quot;: [ 511.0 ],</span></div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;<span class="stringliteral">                                &quot;scale&quot;: [ )&quot; + outputScale + R</span><span class="stringliteral">&quot;( ],</span></div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;<span class="stringliteral">                                &quot;zero_point&quot;: [ )&quot; + outputZeroPoint + R</span><span class="stringliteral">&quot;( ],</span></div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;<span class="stringliteral">                            }</span></div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;<span class="stringliteral">                        },</span></div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;<span class="stringliteral">                        {</span></div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;<span class="stringliteral">                            &quot;shape&quot;: )&quot; + filterShape + R</span><span class="stringliteral">&quot;( ,</span></div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;<span class="stringliteral">                            &quot;type&quot;: &quot;UINT8&quot;,</span></div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;<span class="stringliteral">                            &quot;buffer&quot;: 2,</span></div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;<span class="stringliteral">                            &quot;name&quot;: &quot;filterTensor&quot;,</span></div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;<span class="stringliteral">                            &quot;quantization&quot;: {</span></div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;<span class="stringliteral">                                &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;<span class="stringliteral">                                &quot;max&quot;: [ 255.0 ],</span></div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;<span class="stringliteral">                                &quot;scale&quot;: [ )&quot; + filterScale + R</span><span class="stringliteral">&quot;( ],</span></div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;<span class="stringliteral">                                &quot;zero_point&quot;: [ )&quot; + filterZeroPoint + R</span><span class="stringliteral">&quot;( ],</span></div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;<span class="stringliteral">                            }</span></div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;<span class="stringliteral">                        },</span></div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;<span class="stringliteral">                        {</span></div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;<span class="stringliteral">                            &quot;shape&quot;: )&quot; + biasShape + R</span><span class="stringliteral">&quot;( ,</span></div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;<span class="stringliteral">                            &quot;type&quot;: &quot;INT32&quot;,</span></div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;<span class="stringliteral">                            &quot;buffer&quot;: 3,</span></div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;<span class="stringliteral">                            &quot;name&quot;: &quot;biasTensor&quot;,</span></div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;<span class="stringliteral">                            &quot;quantization&quot;: {</span></div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;<span class="stringliteral">                                &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;<span class="stringliteral">                                &quot;max&quot;: [ 255.0 ],</span></div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;<span class="stringliteral">                                &quot;scale&quot;: [ 1.0 ],</span></div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;<span class="stringliteral">                                &quot;zero_point&quot;: [ 0 ],</span></div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;<span class="stringliteral">                            }</span></div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;<span class="stringliteral">                        }</span></div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;<span class="stringliteral">                    ],</span></div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;<span class="stringliteral">                    &quot;inputs&quot;: [ 0 ],</span></div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;<span class="stringliteral">                    &quot;outputs&quot;: [ 1 ],</span></div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;<span class="stringliteral">                    &quot;operators&quot;: [</span></div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;<span class="stringliteral">                        {</span></div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;<span class="stringliteral">                            &quot;opcode_index&quot;: 0,</span></div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;<span class="stringliteral">                            &quot;inputs&quot;: [ 0, 2, 3 ],</span></div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;<span class="stringliteral">                            &quot;outputs&quot;: [ 1 ],</span></div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;<span class="stringliteral">                            &quot;builtin_options_type&quot;: &quot;Conv2DOptions&quot;,</span></div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;<span class="stringliteral">                            &quot;builtin_options&quot;: {</span></div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;<span class="stringliteral">                                &quot;padding&quot;: &quot;SAME&quot;,</span></div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;<span class="stringliteral">                                &quot;stride_w&quot;: )&quot; + strides + R</span><span class="stringliteral">&quot;(,</span></div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;<span class="stringliteral">                                &quot;stride_h&quot;: )&quot; + strides + R</span><span class="stringliteral">&quot;(,</span></div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;<span class="stringliteral">                                &quot;fused_activation_function&quot;: )&quot; + activation + R</span><span class="stringliteral">&quot;(</span></div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;<span class="stringliteral">                            },</span></div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;<span class="stringliteral">                            &quot;custom_options_format&quot;: &quot;FLEXBUFFERS&quot;</span></div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;<span class="stringliteral">                        }</span></div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;<span class="stringliteral">                    ],</span></div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;<span class="stringliteral">                } ],</span></div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;<span class="stringliteral">                &quot;buffers&quot; : [</span></div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;<span class="stringliteral">                    { },</span></div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;<span class="stringliteral">                    { },</span></div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;<span class="stringliteral">                    { &quot;data&quot;: )&quot; + filterData + R</span><span class="stringliteral">&quot;(, },</span></div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;<span class="stringliteral">                    { &quot;data&quot;: )&quot; + biasData + R</span><span class="stringliteral">&quot;(, },</span></div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;<span class="stringliteral">                ]</span></div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;<span class="stringliteral">            }</span></div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;<span class="stringliteral">        )&quot;;</span></div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;<span class="stringliteral">        SetupSingleInputSingleOutput(</span><span class="stringliteral">&quot;inputTensor&quot;</span>, <span class="stringliteral">&quot;outputTensor&quot;</span>);</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;};</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;<span class="keyword">struct </span>SimpleConv2DWithBiasesFixture : Conv2DWithBiasesFixture</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;{</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    SimpleConv2DWithBiasesFixture()</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    : Conv2DWithBiasesFixture(<span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>,    <span class="comment">// inputShape</span></div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;                              <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>,    <span class="comment">// outputShape</span></div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;                              <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>,    <span class="comment">// filterShape</span></div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;                              <span class="stringliteral">&quot;[ 2,1, 0,6 ]&quot;</span>,      <span class="comment">// filterData</span></div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;                              <span class="stringliteral">&quot;[ 1 ]&quot;</span>,             <span class="comment">// biasShape</span></div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;                              <span class="stringliteral">&quot;[ 10, 0, 0, 0 ]&quot;</span>,   <span class="comment">// biasData</span></div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;                              <span class="stringliteral">&quot;1&quot;</span>)                 <span class="comment">// stride w and h</span></div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;    {}</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;};</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;<a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(SimpleConv2DWithBiasesFixture, <span class="stringliteral">&quot;ParseConv2DWithBias&quot;</span>)</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;{</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;    RunTest&lt;4, armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;        0,</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;        {</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;            1, 2,</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;            3, 4,</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;        <span class="comment">// because of the output scaling we need to take half of the values</span></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;            (1*2 + 2*1 + 3*0 + 4*6 + 10)/2,</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;            (2*2 + 0*1 + 4*0 + 0*6 + 10)/2,</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;            (3*2 + 4*1 + 0*0 + 0*6 + 10)/2,</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;            (4*2 + 0*1 + 0*0 + 0*6 + 10)/2</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;</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;<span class="keyword">struct </span>DynamicConv2DWithBiasesFixture : Conv2DWithBiasesFixture</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;{</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    DynamicConv2DWithBiasesFixture()</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;        : Conv2DWithBiasesFixture(<span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>,    <span class="comment">// inputShape</span></div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;                                  <span class="stringliteral">&quot;[ ]&quot;</span>,              <span class="comment">// outputShape</span></div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;                                  <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>,    <span class="comment">// filterShape</span></div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;                                  <span class="stringliteral">&quot;[ 2,1, 0,6 ]&quot;</span>,      <span class="comment">// filterData</span></div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;                                  <span class="stringliteral">&quot;[ 1 ]&quot;</span>,             <span class="comment">// biasShape</span></div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;                                  <span class="stringliteral">&quot;[ 10, 0, 0, 0 ]&quot;</span>,   <span class="comment">// biasData</span></div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;                                  <span class="stringliteral">&quot;1&quot;</span>)                 <span class="comment">// stride w and h</span></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;};</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;<a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DynamicConv2DWithBiasesFixture, <span class="stringliteral">&quot;ParseDynamicConv2DWithBias&quot;</span>)</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;{</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    RunTest&lt;4,</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        armnn::DataType::QAsymmU8&gt;(0,</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;                                   { { <span class="stringliteral">&quot;inputTensor&quot;</span>, { 1, 2, 3, 4, } } },</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;                                   { { <span class="stringliteral">&quot;outputTensor&quot;</span>, {   (1*2 + 2*1 + 3*0 + 4*6 + 10)/2,</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;                                                           (2*2 + 0*1 + 4*0 + 0*6 + 10)/2,</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;                                                           (3*2 + 4*1 + 0*0 + 0*6 + 10)/2,</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;                                                           (4*2 + 0*1 + 0*0 + 0*6 + 10)/2} } },</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;                                   <span class="keyword">true</span>);</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;}</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;<span class="keyword">struct </span>Conv2DShapeTestFixture : Conv2DWithBiasesFixture</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;{</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    <span class="keyword">static</span> std::string GenerateInts(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> n)</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    {</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;        std::stringstream ss;</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;        ss &lt;&lt; <span class="stringliteral">&quot; [ &quot;</span>;</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;        <span class="keywordflow">for</span>( <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i=0; i&lt;n; ++i ) {</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;            <span class="keywordflow">if</span> (i &gt; 0 )</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;            {</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;                ss &lt;&lt; <span class="stringliteral">&quot; , &quot;</span>;</div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;            }</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;            ss &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; (i%256);</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;        }</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;        ss &lt;&lt; <span class="stringliteral">&quot; ] &quot;</span>;</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;        <span class="keywordflow">return</span> ss.str();</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    }</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;    Conv2DShapeTestFixture()</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    : Conv2DWithBiasesFixture(<span class="stringliteral">&quot;[ 1, 224, 224, 3 ]&quot;</span>,    <span class="comment">// inputShape</span></div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;                              <span class="stringliteral">&quot;[ 1, 112, 112, 32 ]&quot;</span>,   <span class="comment">// outputShape</span></div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;                              <span class="stringliteral">&quot;[ 32, 3, 3, 3 ]&quot;</span>,       <span class="comment">// filterShape</span></div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;                              GenerateInts(32*3*3*3),  <span class="comment">// filterData</span></div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;                              <span class="stringliteral">&quot;[ 32 ]&quot;</span>,                <span class="comment">// biasShape</span></div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;                              GenerateInts(32*4),      <span class="comment">// biasData</span></div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;                              <span class="stringliteral">&quot;2&quot;</span>)                     <span class="comment">// stride w and h</span></div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    {}</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;</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;<a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(Conv2DShapeTestFixture, <span class="stringliteral">&quot;ParseConv2D_112x112_out&quot;</span>)</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;{</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;</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;<span class="keyword">struct </span>ReluConv2DWithBiasesFixture : Conv2DWithBiasesFixture</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;    ReluConv2DWithBiasesFixture()</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    : Conv2DWithBiasesFixture(<span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>,    <span class="comment">// inputShape</span></div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;                              <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>,    <span class="comment">// outputShape</span></div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;                              <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>,    <span class="comment">// filterShape</span></div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;                              <span class="stringliteral">&quot;[ 2,1, 0,6 ]&quot;</span>,      <span class="comment">// filterData</span></div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;                              <span class="stringliteral">&quot;[ 1 ]&quot;</span>,             <span class="comment">// biasShape</span></div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;                              <span class="stringliteral">&quot;[ 16, 0, 0, 0 ]&quot;</span>,   <span class="comment">// biasData</span></div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;                              <span class="stringliteral">&quot;1&quot;</span>,                 <span class="comment">// stride w and h</span></div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;                              <span class="stringliteral">&quot;RELU&quot;</span>,              <span class="comment">// activation</span></div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;                              <span class="stringliteral">&quot;1.0&quot;</span>,               <span class="comment">// filter scale</span></div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;                              <span class="stringliteral">&quot;4&quot;</span>,                 <span class="comment">// filter zero point</span></div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;                              <span class="stringliteral">&quot;2.0&quot;</span>,               <span class="comment">// output scale</span></div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;                              <span class="stringliteral">&quot;20&quot;</span>)                <span class="comment">// output zero point</span></div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    {}</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;};</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="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(ReluConv2DWithBiasesFixture, <span class="stringliteral">&quot;ParseConv2DAndReluWithBias&quot;</span>)</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;{</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    uint8_t bias = 16;</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;    uint8_t outZero = 20;</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    uint8_t fz = 4; <span class="comment">// filter zero point</span></div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    RunTest&lt;4, armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;        0,</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;        {</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;            1, 2,</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;            4, 8,</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;        },</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;        <span class="comment">// factors to consider:</span></div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;        <span class="comment">// - the filter zero point is non zero, hence the (x-fz)</span></div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;        <span class="comment">// - the output scale is 2 hence the /2</span></div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;        <span class="comment">// - output zero point is non zero, hence the +outZero</span></div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;        <span class="comment">// - RELU cuts negative values and then we add the output zero point</span></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">  329</span>&#160;            std::max(outZero, static_cast&lt;uint8_t&gt;((1*(2-fz) + 2*(1-fz) + 4*(0-fz) + 8*(6-fz) + bias)/2 + outZero)),</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;            std::max(outZero, static_cast&lt;uint8_t&gt;((2*(2-fz) + 0*(1-fz) + 8*(0-fz) + 0*(6-fz) + bias)/2 + outZero)),</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;            std::max(outZero, static_cast&lt;uint8_t&gt;((4*(2-fz) + 8*(1-fz) + 0*(0-fz) + 0*(6-fz) + bias)/2 + outZero)),</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;            std::max(outZero, static_cast&lt;uint8_t&gt;((8*(2-fz) + 0*(1-fz) + 0*(0-fz) + 0*(6-fz) + bias)/2 + outZero))</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;}</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;<span class="keyword">struct </span>Relu6Conv2DWithBiasesFixture : Conv2DWithBiasesFixture</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;{</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    Relu6Conv2DWithBiasesFixture()</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    : Conv2DWithBiasesFixture(<span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>,    <span class="comment">// inputShape</span></div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;                              <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>,    <span class="comment">// outputShape</span></div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;                              <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>,    <span class="comment">// filterShape</span></div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;                              <span class="stringliteral">&quot;[ 2,1, 0,6 ]&quot;</span>,      <span class="comment">// filterData</span></div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;                              <span class="stringliteral">&quot;[ 1 ]&quot;</span>,             <span class="comment">// biasShape</span></div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;                              <span class="stringliteral">&quot;[ 0, 0, 0, 0 ]&quot;</span>,    <span class="comment">// biasData</span></div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;                              <span class="stringliteral">&quot;1&quot;</span>,                 <span class="comment">// stride w and h</span></div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;                              <span class="stringliteral">&quot;RELU6&quot;</span>,             <span class="comment">// activation</span></div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;                              <span class="stringliteral">&quot;1.0&quot;</span>,               <span class="comment">// filter scale</span></div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;                              <span class="stringliteral">&quot;0&quot;</span>,                 <span class="comment">// filter zero point</span></div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;                              <span class="stringliteral">&quot;2.0&quot;</span>,               <span class="comment">// output scale</span></div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;                              <span class="stringliteral">&quot;0&quot;</span>)                 <span class="comment">// output zero point</span></div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;    {}</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;};</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;<a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(Relu6Conv2DWithBiasesFixture, <span class="stringliteral">&quot;ParseConv2DAndRelu6WithBias&quot;</span>)</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;{</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;    uint8_t relu6Min = 6 / 2; <span class="comment">// divide by output scale</span></div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;    RunTest&lt;4, armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;        0,</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;        {</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;            1, 2,</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;            4, 1,</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;        },</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;        <span class="comment">// factors to consider:</span></div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;        <span class="comment">// - the output scale is 2 hence the /2</span></div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;        <span class="comment">// - RELU6 cuts output values at +6</span></div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;        {</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;            std::min(relu6Min, static_cast&lt;uint8_t&gt;((1*2 + 2*1 + 4*0 + 1*6)/2)),</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;            std::min(relu6Min, static_cast&lt;uint8_t&gt;((2*2 + 0*1 + 1*0 + 0*6)/2)),</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;            std::min(relu6Min, static_cast&lt;uint8_t&gt;((4*2 + 1*1 + 0*0 + 0*6)/2)),</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;            std::min(relu6Min, static_cast&lt;uint8_t&gt;((1*2 + 0*1 + 0*0 + 0*6)/2))</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;}</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;<span class="keyword">struct </span>PerChannelConv2DFixture : <span class="keyword">public</span> <a class="code" href="struct_parser_flatbuffers_fixture.xhtml">ParserFlatbuffersFixture</a></div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;{</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    <span class="keyword">explicit</span> PerChannelConv2DFixture()</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;    {</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;        m_JsonString = R<span class="stringliteral">&quot;(</span></div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;<span class="stringliteral">        {</span></div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;<span class="stringliteral">            &quot;version&quot;: 3,</span></div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;<span class="stringliteral">            &quot;operator_codes&quot;: [</span></div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;<span class="stringliteral">                {</span></div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;<span class="stringliteral">                    &quot;builtin_code&quot;: &quot;CONV_2D&quot;,</span></div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;<span class="stringliteral">                    &quot;version&quot;: 3</span></div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;<span class="stringliteral">                }</span></div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;<span class="stringliteral">            ],</span></div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;<span class="stringliteral">            &quot;subgraphs&quot;: [</span></div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;<span class="stringliteral">                {</span></div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;<span class="stringliteral">                    &quot;tensors&quot;: [</span></div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;<span class="stringliteral">                        {</span></div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;<span class="stringliteral">                            &quot;shape&quot;: [</span></div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;<span class="stringliteral">                                1,</span></div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;<span class="stringliteral">                                4,</span></div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;<span class="stringliteral">                                4,</span></div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;<span class="stringliteral">                                2</span></div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;<span class="stringliteral">                            ],</span></div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;<span class="stringliteral">                            &quot;type&quot;: 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],</span></div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;<span class="stringliteral">                                &quot;zero_point&quot;: [</span></div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;<span class="stringliteral">                                    1</span></div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;<span class="stringliteral">                                ],</span></div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;<span class="stringliteral">                                &quot;details_type&quot;: &quot;NONE&quot;,</span></div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;<span class="stringliteral">                                &quot;quantized_dimension&quot;: 0</span></div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;<span class="stringliteral">                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0,</span></div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;<span class="stringliteral">                                    0,</span></div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;<span class="stringliteral">                                    0,</span></div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;<span class="stringliteral">                                    0</span></div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;<span class="stringliteral">                                ],</span></div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;<span class="stringliteral">                                &quot;details_type&quot;: &quot;NONE&quot;,</span></div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;<span class="stringliteral">                                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class="ttname"><a href="struct_parser_flatbuffers_fixture.xhtml">ParserFlatbuffersFixture</a></div><div class="ttdef"><b>Definition:</b> <a href="_parser_flatbuffers_fixture_8hpp_source.xhtml#l00036">ParserFlatbuffersFixture.hpp:36</a></div></div>
<div class="ttc" id="_mem_copy_tests_8cpp_xhtml_a3df1acc0ccc35bce0bd6c027e23e2c45"><div class="ttname"><a href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a></div><div class="ttdeci">TEST_CASE_FIXTURE(ClContextControlFixture, &quot;CopyBetweenNeonAndGpu&quot;)</div><div class="ttdef"><b>Definition:</b> <a href="_mem_copy_tests_8cpp_source.xhtml#l00045">MemCopyTests.cpp:45</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="struct_parser_flatbuffers_fixture_xhtml_a2bb4ea256fbbf6d53068ca93bb4bc95c"><div class="ttname"><a href="struct_parser_flatbuffers_fixture.xhtml#a2bb4ea256fbbf6d53068ca93bb4bc95c">ParserFlatbuffersFixture::SetupSingleInputSingleOutput</a></div><div class="ttdeci">void SetupSingleInputSingleOutput(const std::string &amp;inputName, const std::string &amp;outputName)</div><div class="ttdef"><b>Definition:</b> <a href="_parser_flatbuffers_fixture_8hpp_source.xhtml#l00153">ParserFlatbuffersFixture.hpp:153</a></div></div>
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