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<div class="title">Conv2D.cpp</div> </div>
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<a href="armnn_tf_lite_parser_2test_2_conv2_d_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 &lt;boost/test/unit_test.hpp&gt;</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_parser_flatbuffers_fixture_8hpp.xhtml">ParserFlatbuffersFixture.hpp</a>&quot;</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="preprocessor">#include &quot;../TfLiteParser.hpp&quot;</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="preprocessor">#include &lt;sstream&gt;</span></div><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;<a class="code" href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(TensorflowLiteParser)</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">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="l00014"></a><span class="lineno"> 14</span>&#160;{</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160; <span class="keyword">explicit</span> SimpleConv2DFixture()</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160; {</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160; <a class="code" href="struct_parser_flatbuffers_fixture.xhtml#a803c86dca3acef653c1cc481a27be7a9">m_JsonString</a> = R<span class="stringliteral">&quot;(</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<span class="stringliteral"> {</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="stringliteral"> &quot;version&quot;: 3,</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;<span class="stringliteral"> &quot;operator_codes&quot;: [ { &quot;builtin_code&quot;: &quot;CONV_2D&quot; } ],</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="stringliteral"> &quot;subgraphs&quot;: [ {</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;<span class="stringliteral"> &quot;tensors&quot;: [</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="stringliteral"> {</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="stringliteral"> &quot;shape&quot;: [ 1, 3, 3, 1 ],</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;<span class="stringliteral"> &quot;type&quot;: &quot;UINT8&quot;,</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;<span class="stringliteral"> &quot;buffer&quot;: 0,</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="stringliteral"> &quot;name&quot;: &quot;inputTensor&quot;,</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;<span class="stringliteral"> &quot;quantization&quot;: {</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;<span class="stringliteral"> &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;<span class="stringliteral"> &quot;max&quot;: [ 255.0 ],</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;<span class="stringliteral"> &quot;scale&quot;: [ 1.0 ],</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;<span class="stringliteral"> &quot;zero_point&quot;: [ 0 ],</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"> },</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;<span class="stringliteral"> {</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;<span class="stringliteral"> &quot;shape&quot;: [ 1, 1, 1, 1 ],</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;<span class="stringliteral"> &quot;type&quot;: &quot;UINT8&quot;,</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;<span class="stringliteral"> &quot;buffer&quot;: 1,</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;<span class="stringliteral"> &quot;name&quot;: &quot;outputTensor&quot;,</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;<span class="stringliteral"> &quot;quantization&quot;: {</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160;<span class="stringliteral"> &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;<span class="stringliteral"> &quot;max&quot;: [ 511.0 ],</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160;<span class="stringliteral"> &quot;scale&quot;: [ 2.0 ],</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160;<span class="stringliteral"> &quot;zero_point&quot;: [ 0 ],</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"> },</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;<span class="stringliteral"> {</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160;<span class="stringliteral"> &quot;shape&quot;: [ 1, 3, 3, 1 ],</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160;<span class="stringliteral"> &quot;type&quot;: &quot;UINT8&quot;,</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160;<span class="stringliteral"> &quot;buffer&quot;: 2,</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160;<span class="stringliteral"> &quot;name&quot;: &quot;filterTensor&quot;,</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160;<span class="stringliteral"> &quot;quantization&quot;: {</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160;<span class="stringliteral"> &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160;<span class="stringliteral"> &quot;max&quot;: [ 255.0 ],</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160;<span class="stringliteral"> &quot;scale&quot;: [ 1.0 ],</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160;<span class="stringliteral"> &quot;zero_point&quot;: [ 0 ],</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"> }</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160;<span class="stringliteral"> ],</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160;<span class="stringliteral"> &quot;inputs&quot;: [ 0 ],</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160;<span class="stringliteral"> &quot;outputs&quot;: [ 1 ],</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160;<span class="stringliteral"> &quot;operators&quot;: [</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160;<span class="stringliteral"> {</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160;<span class="stringliteral"> &quot;opcode_index&quot;: 0,</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160;<span class="stringliteral"> &quot;inputs&quot;: [ 0, 2 ],</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160;<span class="stringliteral"> &quot;outputs&quot;: [ 1 ],</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;<span class="stringliteral"> &quot;builtin_options_type&quot;: &quot;Conv2DOptions&quot;,</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;<span class="stringliteral"> &quot;builtin_options&quot;: {</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160;<span class="stringliteral"> &quot;padding&quot;: &quot;VALID&quot;,</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;<span class="stringliteral"> &quot;stride_w&quot;: 1,</span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160;<span class="stringliteral"> &quot;stride_h&quot;: 1,</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160;<span class="stringliteral"> &quot;fused_activation_function&quot;: &quot;NONE&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"> &quot;custom_options_format&quot;: &quot;FLEXBUFFERS&quot;</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"> ],</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"> &quot;buffers&quot; : [</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160;<span class="stringliteral"> { },</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"> { &quot;data&quot;: [ 2,1,0, 6,2,1, 4,1,2 ], },</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"> ]</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;<span class="stringliteral"> }</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160;<span class="stringliteral"> )&quot;;</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</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="l00087"></a><span class="lineno"> 87</span>&#160; }</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;};</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"><a class="line" href="armnn_tf_lite_parser_2test_2_conv2_d_8cpp.xhtml#afcc317a537dfa8ab47071d5c464bad43"> 90</a></span>&#160;<a class="code" href="armnn_onnx_parser_2test_2_conv2_d_8cpp.xhtml#aae03717a46f9e4b8ad98831cc73687ce">BOOST_FIXTURE_TEST_CASE</a>( ParseSimpleConv2D, SimpleConv2DFixture )</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160;{</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; RunTest&lt;4, armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; 0,</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; {</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; 1, 2, 3,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; 4, 5, 6,</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; 7, 8, 9,</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; <span class="comment">// because of the output scaling we need to take half of the values</span></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; (1*2 + 2*1 + 3*0 +</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; 4*6 + 5*2 + 6*1 +</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; 7*4 + 8*1 + 9*2) /2</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;}</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">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="l00108"></a><span class="lineno"> 108</span>&#160;{</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; <span class="keyword">explicit</span> Conv2DWithBiasesFixture(<span class="keyword">const</span> std::string &amp; inputShape,</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <span class="keyword">const</span> std::string &amp; outputShape,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <span class="keyword">const</span> std::string &amp; filterShape,</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <span class="keyword">const</span> std::string &amp; filterData,</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; <span class="keyword">const</span> std::string &amp; biasShape,</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; <span class="keyword">const</span> std::string &amp; biasData,</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; <span class="keyword">const</span> std::string &amp; strides,</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="keyword">const</span> std::string &amp; activation=<span class="stringliteral">&quot;NONE&quot;</span>,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</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="l00118"></a><span class="lineno"> 118</span>&#160; <span class="keyword">const</span> std::string &amp; filterZeroPoint=<span class="stringliteral">&quot;0&quot;</span>,</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</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="l00120"></a><span class="lineno"> 120</span>&#160; <span class="keyword">const</span> std::string &amp; outputZeroPoint=<span class="stringliteral">&quot;0&quot;</span>)</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; {</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; m_JsonString = R<span class="stringliteral">&quot;(</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160;<span class="stringliteral"> {</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160;<span class="stringliteral"> &quot;version&quot;: 3,</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160;<span class="stringliteral"> &quot;operator_codes&quot;: [ { &quot;builtin_code&quot;: &quot;CONV_2D&quot; } ],</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160;<span class="stringliteral"> &quot;subgraphs&quot;: [ {</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160;<span class="stringliteral"> &quot;tensors&quot;: [</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160;<span class="stringliteral"> {</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160;<span class="stringliteral"> &quot;shape&quot;: )&quot; + inputShape + R</span><span class="stringliteral">&quot;(,</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160;<span class="stringliteral"> &quot;type&quot;: &quot;UINT8&quot;,</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160;<span class="stringliteral"> &quot;buffer&quot;: 0,</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160;<span class="stringliteral"> &quot;name&quot;: &quot;inputTensor&quot;,</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160;<span class="stringliteral"> &quot;quantization&quot;: {</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160;<span class="stringliteral"> &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160;<span class="stringliteral"> &quot;max&quot;: [ 255.0 ],</span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160;<span class="stringliteral"> &quot;scale&quot;: [ 1.0 ],</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160;<span class="stringliteral"> &quot;zero_point&quot;: [ 0 ],</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"> },</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160;<span class="stringliteral"> {</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160;<span class="stringliteral"> &quot;shape&quot;: )&quot; + outputShape + R</span><span class="stringliteral">&quot;(,</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160;<span class="stringliteral"> &quot;type&quot;: &quot;UINT8&quot;,</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160;<span class="stringliteral"> &quot;buffer&quot;: 1,</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160;<span class="stringliteral"> &quot;name&quot;: &quot;outputTensor&quot;,</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160;<span class="stringliteral"> &quot;quantization&quot;: {</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160;<span class="stringliteral"> &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160;<span class="stringliteral"> &quot;max&quot;: [ 511.0 ],</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160;<span class="stringliteral"> &quot;scale&quot;: [ )&quot; + outputScale + R</span><span class="stringliteral">&quot;( ],</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160;<span class="stringliteral"> &quot;zero_point&quot;: [ )&quot; + outputZeroPoint + R</span><span class="stringliteral">&quot;( ],</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"> },</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160;<span class="stringliteral"> {</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160;<span class="stringliteral"> &quot;shape&quot;: )&quot; + filterShape + R</span><span class="stringliteral">&quot;( ,</span></div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160;<span class="stringliteral"> &quot;type&quot;: &quot;UINT8&quot;,</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160;<span class="stringliteral"> &quot;buffer&quot;: 2,</span></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160;<span class="stringliteral"> &quot;name&quot;: &quot;filterTensor&quot;,</span></div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160;<span class="stringliteral"> &quot;quantization&quot;: {</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160;<span class="stringliteral"> &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160;<span class="stringliteral"> &quot;max&quot;: [ 255.0 ],</span></div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160;<span class="stringliteral"> &quot;scale&quot;: [ )&quot; + filterScale + R</span><span class="stringliteral">&quot;( ],</span></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160;<span class="stringliteral"> &quot;zero_point&quot;: [ )&quot; + filterZeroPoint + R</span><span class="stringliteral">&quot;( ],</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"> },</span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160;<span class="stringliteral"> {</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160;<span class="stringliteral"> &quot;shape&quot;: )&quot; + biasShape + R</span><span class="stringliteral">&quot;( ,</span></div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160;<span class="stringliteral"> &quot;type&quot;: &quot;INT32&quot;,</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160;<span class="stringliteral"> &quot;buffer&quot;: 3,</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160;<span class="stringliteral"> &quot;name&quot;: &quot;biasTensor&quot;,</span></div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160;<span class="stringliteral"> &quot;quantization&quot;: {</span></div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160;<span class="stringliteral"> &quot;min&quot;: [ 0.0 ],</span></div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160;<span class="stringliteral"> &quot;max&quot;: [ 255.0 ],</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160;<span class="stringliteral"> &quot;scale&quot;: [ 1.0 ],</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160;<span class="stringliteral"> &quot;zero_point&quot;: [ 0 ],</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"> }</span></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160;<span class="stringliteral"> ],</span></div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160;<span class="stringliteral"> &quot;inputs&quot;: [ 0 ],</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160;<span class="stringliteral"> &quot;outputs&quot;: [ 1 ],</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160;<span class="stringliteral"> &quot;operators&quot;: [</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160;<span class="stringliteral"> {</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160;<span class="stringliteral"> &quot;opcode_index&quot;: 0,</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160;<span class="stringliteral"> &quot;inputs&quot;: [ 0, 2, 3 ],</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160;<span class="stringliteral"> &quot;outputs&quot;: [ 1 ],</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160;<span class="stringliteral"> &quot;builtin_options_type&quot;: &quot;Conv2DOptions&quot;,</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160;<span class="stringliteral"> &quot;builtin_options&quot;: {</span></div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160;<span class="stringliteral"> &quot;padding&quot;: &quot;SAME&quot;,</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160;<span class="stringliteral"> &quot;stride_w&quot;: )&quot; + strides + R</span><span class="stringliteral">&quot;(,</span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160;<span class="stringliteral"> &quot;stride_h&quot;: )&quot; + strides + R</span><span class="stringliteral">&quot;(,</span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</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="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"> &quot;custom_options_format&quot;: &quot;FLEXBUFFERS&quot;</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"> ],</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"> &quot;buffers&quot; : [</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160;<span class="stringliteral"> { },</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160;<span class="stringliteral"> { },</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160;<span class="stringliteral"> { &quot;data&quot;: )&quot; + filterData + R</span><span class="stringliteral">&quot;(, },</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160;<span class="stringliteral"> { &quot;data&quot;: )&quot; + biasData + R</span><span class="stringliteral">&quot;(, },</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160;<span class="stringliteral"> ]</span></div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160;<span class="stringliteral"> }</span></div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160;<span class="stringliteral"> )&quot;;</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</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="l00204"></a><span class="lineno"> 204</span>&#160; }</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;</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160;<span class="keyword">struct </span>SimpleConv2DWithBiasesFixture : Conv2DWithBiasesFixture</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160;{</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; SimpleConv2DWithBiasesFixture()</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; : Conv2DWithBiasesFixture(<span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; <span class="stringliteral">&quot;[ 2,1, 0,6 ]&quot;</span>, <span class="comment">// filterData</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">// biasShape</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160; <span class="stringliteral">&quot;[ 10, 0, 0, 0 ]&quot;</span>, <span class="comment">// biasData</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; <span class="stringliteral">&quot;1&quot;</span>) <span class="comment">// stride w and h</span></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;};</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"><a class="line" href="armnn_tf_lite_parser_2test_2_conv2_d_8cpp.xhtml#a1f8274795936acc6692b9e18a2d7f7a0"> 220</a></span>&#160;<a class="code" href="armnn_onnx_parser_2test_2_conv2_d_8cpp.xhtml#aae03717a46f9e4b8ad98831cc73687ce">BOOST_FIXTURE_TEST_CASE</a>( ParseConv2DWithBias, SimpleConv2DWithBiasesFixture )</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160;{</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; RunTest&lt;4, armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; 0,</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; {</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; 1, 2,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; 3, 4,</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; <span class="comment">// because of the output scaling we need to take half of the values</span></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; (1*2 + 2*1 + 3*0 + 4*6 + 10)/2,</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; (2*2 + 0*1 + 4*0 + 0*6 + 10)/2,</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; (3*2 + 4*1 + 0*0 + 0*6 + 10)/2,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; (4*2 + 0*1 + 0*0 + 0*6 + 10)/2</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;}</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;<span class="keyword">struct </span>Conv2DShapeTestFixture : Conv2DWithBiasesFixture</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; <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="l00240"></a><span class="lineno"> 240</span>&#160; {</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; std::stringstream ss;</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; ss &lt;&lt; <span class="stringliteral">&quot; [ &quot;</span>;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</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="l00244"></a><span class="lineno"> 244</span>&#160; <span class="keywordflow">if</span> (i &gt; 0 )</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; ss &lt;&lt; <span class="stringliteral">&quot; , &quot;</span>;</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; ss &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; (i%256);</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; ss &lt;&lt; <span class="stringliteral">&quot; ] &quot;</span>;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; <span class="keywordflow">return</span> ss.str();</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; }</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160;</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; Conv2DShapeTestFixture()</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; : Conv2DWithBiasesFixture(<span class="stringliteral">&quot;[ 1, 224, 224, 3 ]&quot;</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; <span class="stringliteral">&quot;[ 1, 112, 112, 32 ]&quot;</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; <span class="stringliteral">&quot;[ 32, 3, 3, 3 ]&quot;</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; GenerateInts(32*3*3*3), <span class="comment">// filterData</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; <span class="stringliteral">&quot;[ 32 ]&quot;</span>, <span class="comment">// biasShape</span></div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160; GenerateInts(32*4), <span class="comment">// biasData</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; <span class="stringliteral">&quot;2&quot;</span>) <span class="comment">// stride w and h</span></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;};</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"><a class="line" href="armnn_tf_lite_parser_2test_2_conv2_d_8cpp.xhtml#a37807b86838def29aa4a342b5ee11a1e"> 265</a></span>&#160;<a class="code" href="armnn_onnx_parser_2test_2_conv2_d_8cpp.xhtml#aae03717a46f9e4b8ad98831cc73687ce">BOOST_FIXTURE_TEST_CASE</a>( ParseConv2D_112x112_out, Conv2DShapeTestFixture )</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;}</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;<span class="keyword">struct </span>ReluConv2DWithBiasesFixture : Conv2DWithBiasesFixture</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"> 271</span>&#160; ReluConv2DWithBiasesFixture()</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; : Conv2DWithBiasesFixture(<span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160; <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160; <span class="stringliteral">&quot;[ 2,1, 0,6 ]&quot;</span>, <span class="comment">// filterData</span></div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160; <span class="stringliteral">&quot;[ 1 ]&quot;</span>, <span class="comment">// biasShape</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; <span class="stringliteral">&quot;[ 16, 0, 0, 0 ]&quot;</span>, <span class="comment">// biasData</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; <span class="stringliteral">&quot;1&quot;</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; <span class="stringliteral">&quot;RELU&quot;</span>, <span class="comment">// activation</span></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; <span class="stringliteral">&quot;1.0&quot;</span>, <span class="comment">// filter scale</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; <span class="stringliteral">&quot;4&quot;</span>, <span class="comment">// filter zero point</span></div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160; <span class="stringliteral">&quot;2.0&quot;</span>, <span class="comment">// output scale</span></div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; <span class="stringliteral">&quot;20&quot;</span>) <span class="comment">// output zero point</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; {}</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160;};</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"><a class="line" href="armnn_tf_lite_parser_2test_2_conv2_d_8cpp.xhtml#aeae31b1eb962892a52153424119c87e9"> 287</a></span>&#160;<a class="code" href="armnn_onnx_parser_2test_2_conv2_d_8cpp.xhtml#aae03717a46f9e4b8ad98831cc73687ce">BOOST_FIXTURE_TEST_CASE</a>( ParseConv2DAndReluWithBias, ReluConv2DWithBiasesFixture )</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; uint8_t bias = 16;</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; uint8_t outZero = 20;</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; uint8_t fz = 4; <span class="comment">// filter zero point</span></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; RunTest&lt;4, armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; 0,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; {</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; 1, 2,</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; 4, 8,</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"> 299</span>&#160; <span class="comment">// factors to consider:</span></div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160; <span class="comment">// - the filter zero point is non zero, hence the (x-fz)</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160; <span class="comment">// - the output scale is 2 hence the /2</span></div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; <span class="comment">// - output zero point is non zero, hence the +outZero</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; <span class="comment">// - RELU cuts negative values and then we add the output zero point</span></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; {</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</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="l00306"></a><span class="lineno"> 306</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="l00307"></a><span class="lineno"> 307</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="l00308"></a><span class="lineno"> 308</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="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;</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160;<span class="keyword">struct </span>Relu6Conv2DWithBiasesFixture : Conv2DWithBiasesFixture</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160;{</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; Relu6Conv2DWithBiasesFixture()</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; : Conv2DWithBiasesFixture(<span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160; <span class="stringliteral">&quot;[ 1, 2, 2, 1 ]&quot;</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160; <span class="stringliteral">&quot;[ 2,1, 0,6 ]&quot;</span>, <span class="comment">// filterData</span></div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160; <span class="stringliteral">&quot;[ 1 ]&quot;</span>, <span class="comment">// biasShape</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160; <span class="stringliteral">&quot;[ 0, 0, 0, 0 ]&quot;</span>, <span class="comment">// biasData</span></div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; <span class="stringliteral">&quot;1&quot;</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; <span class="stringliteral">&quot;RELU6&quot;</span>, <span class="comment">// activation</span></div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; <span class="stringliteral">&quot;1.0&quot;</span>, <span class="comment">// filter scale</span></div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; <span class="stringliteral">&quot;0&quot;</span>, <span class="comment">// filter zero point</span></div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160; <span class="stringliteral">&quot;2.0&quot;</span>, <span class="comment">// output scale</span></div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; <span class="stringliteral">&quot;0&quot;</span>) <span class="comment">// output zero point</span></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"> 329</span>&#160;</div><div class="line"><a name="l00330"></a><span class="lineno"><a class="line" href="armnn_tf_lite_parser_2test_2_conv2_d_8cpp.xhtml#a16517509d65fc4291eb40bcc285c03fd"> 330</a></span>&#160;<a class="code" href="armnn_onnx_parser_2test_2_conv2_d_8cpp.xhtml#aae03717a46f9e4b8ad98831cc73687ce">BOOST_FIXTURE_TEST_CASE</a>( ParseConv2DAndRelu6WithBias, Relu6Conv2DWithBiasesFixture )</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>&#160;{</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; uint8_t relu6Min = 6 / 2; <span class="comment">// divide by output scale</span></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; RunTest&lt;4, armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; 0,</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160; {</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; 1, 2,</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; 4, 1,</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; },</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160; <span class="comment">// factors to consider:</span></div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; <span class="comment">// - the output scale is 2 hence the /2</span></div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; <span class="comment">// - RELU6 cuts output values at +6</span></div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; {</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</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="l00345"></a><span class="lineno"> 345</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="l00346"></a><span class="lineno"> 346</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="l00347"></a><span class="lineno"> 347</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="l00348"></a><span class="lineno"> 348</span>&#160; });</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160;}</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160;</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</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="struct_parser_flatbuffers_fixture_xhtml_a803c86dca3acef653c1cc481a27be7a9"><div class="ttname"><a href="struct_parser_flatbuffers_fixture.xhtml#a803c86dca3acef653c1cc481a27be7a9">ParserFlatbuffersFixture::m_JsonString</a></div><div class="ttdeci">std::string m_JsonString</div><div class="ttdef"><b>Definition:</b> <a href="_parser_flatbuffers_fixture_8hpp_source.xhtml#l00051">ParserFlatbuffersFixture.hpp:51</a></div></div>
<div class="ttc" id="struct_parser_flatbuffers_fixture_xhtml"><div 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#l00037">ParserFlatbuffersFixture.hpp:37</a></div></div>
<div class="ttc" id="_parser_flatbuffers_fixture_8hpp_xhtml"><div class="ttname"><a href="_parser_flatbuffers_fixture_8hpp.xhtml">ParserFlatbuffersFixture.hpp</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="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#l00094">ParserFlatbuffersFixture.hpp:94</a></div></div>
<div class="ttc" id="armnn_onnx_parser_2test_2_conv2_d_8cpp_xhtml_aae03717a46f9e4b8ad98831cc73687ce"><div class="ttname"><a href="armnn_onnx_parser_2test_2_conv2_d_8cpp.xhtml#aae03717a46f9e4b8ad98831cc73687ce">BOOST_FIXTURE_TEST_CASE</a></div><div class="ttdeci">BOOST_FIXTURE_TEST_CASE(ValidConvTest, SimpleConv2DFixture)</div><div class="ttdef"><b>Definition:</b> <a href="armnn_onnx_parser_2test_2_conv2_d_8cpp_source.xhtml#l00441">Conv2D.cpp:441</a></div></div>
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