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<a href="_depthwise_convolution2_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> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_parser_flatbuffers_fixture_8hpp.xhtml">ParserFlatbuffersFixture.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"><a class="line" href="_depthwise_convolution2_d_8cpp.xhtml#a75c44cab4542630baaefa446e81cbf01"> 9</a></span> <a class="code" href="_depthwise_convolution2_d_8cpp.xhtml#a75c44cab4542630baaefa446e81cbf01">TEST_SUITE</a>(<span class="stringliteral">"TensorflowLiteParser_DepthwiseConvolution2D"</span>)</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> {</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="keyword">struct </span>DepthwiseConvolution2dFixture : <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> {</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>  <span class="keyword">explicit</span> DepthwiseConvolution2dFixture(<span class="keyword">const</span> std::string& inputShape,</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  <span class="keyword">const</span> std::string& outputShape,</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  <span class="keyword">const</span> std::string& filterShape,</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  <span class="keyword">const</span> std::string& filterData,</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  <span class="keyword">const</span> std::string& strides,</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <span class="keyword">const</span> std::string& paddingType,</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <span class="keyword">const</span> std::string biasShape = <span class="stringliteral">""</span>,</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <span class="keyword">const</span> std::string biasData = <span class="stringliteral">""</span>)</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  {</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  std::string inputTensors = <span class="stringliteral">"[ 0, 2 ]"</span>;</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  std::string biasTensor = <span class="stringliteral">""</span>;</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  std::string biasBuffer = <span class="stringliteral">""</span>;</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keywordflow">if</span> (biasShape.size() > 0 && biasData.size() > 0)</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  {</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  inputTensors = <span class="stringliteral">"[ 0, 2, 3 ]"</span>;</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  biasTensor = R<span class="stringliteral">"(</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="stringliteral"> {</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="stringliteral"> "shape": )" + biasShape + R</span><span class="stringliteral">"( ,</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="stringliteral"> "type": "INT32",</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="stringliteral"> "buffer": 3,</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="stringliteral"> "name": "biasTensor",</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="stringliteral"> "quantization": {</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="stringliteral"> "min": [ 0.0 ],</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="stringliteral"> "max": [ 255.0 ],</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="stringliteral"> "scale": [ 1.0 ],</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="stringliteral"> "zero_point": [ 0 ],</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="stringliteral"> } )";</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="stringliteral"> biasBuffer = R</span><span class="stringliteral">"(</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="stringliteral"> { "data": )" + biasData + R</span><span class="stringliteral">"(, }, )";</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="stringliteral"> m_JsonString = R</span><span class="stringliteral">"(</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="stringliteral"> {</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="stringliteral"> "version": 3,</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="stringliteral"> "operator_codes": [ { "builtin_code": "DEPTHWISE_CONV_2D" } ],</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="stringliteral"> "subgraphs": [ {</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="stringliteral"> "tensors": [</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="stringliteral"> {</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="stringliteral"> "shape": )" + inputShape + R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="stringliteral"> "type": "UINT8",</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="stringliteral"> "buffer": 0,</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="stringliteral"> "name": "inputTensor",</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="stringliteral"> "quantization": {</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="stringliteral"> "min": [ 0.0 ],</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="stringliteral"> "max": [ 255.0 ],</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="stringliteral"> "scale": [ 1.0 ],</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="stringliteral"> "zero_point": [ 0 ],</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="stringliteral"> },</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="stringliteral"> {</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="stringliteral"> "shape": )" + outputShape + R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> <span class="stringliteral"> "type": "UINT8",</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> <span class="stringliteral"> "buffer": 1,</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <span class="stringliteral"> "name": "outputTensor",</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> <span class="stringliteral"> "quantization": {</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> <span class="stringliteral"> "min": [ 0.0 ],</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> <span class="stringliteral"> "max": [ 511.0 ],</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> <span class="stringliteral"> "scale": [ 2.0 ],</span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> <span class="stringliteral"> "zero_point": [ 0 ],</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> <span class="stringliteral"> },</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="stringliteral"> {</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <span class="stringliteral"> "shape": )" + filterShape + R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> <span class="stringliteral"> "type": "UINT8",</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="stringliteral"> "buffer": 2,</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> <span class="stringliteral"> "name": "filterTensor",</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="stringliteral"> "quantization": {</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="stringliteral"> "min": [ 0.0 ],</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="stringliteral"> "max": [ 255.0 ],</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="stringliteral"> "scale": [ 1.0 ],</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="stringliteral"> "zero_point": [ 0 ],</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> <span class="stringliteral"> }, )" + biasTensor + R</span><span class="stringliteral">"(</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="stringliteral"> ],</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> <span class="stringliteral"> "inputs": [ 0 ],</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> <span class="stringliteral"> "outputs": [ 1 ],</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> <span class="stringliteral"> "operators": [</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> <span class="stringliteral"> {</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> <span class="stringliteral"> "opcode_index": 0,</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="stringliteral"> "inputs": )" + inputTensors + R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="stringliteral"> "outputs": [ 1 ],</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="stringliteral"> "builtin_options_type": "DepthwiseConv2DOptions",</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="stringliteral"> "builtin_options": {</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> <span class="stringliteral"> "padding": ")" + paddingType + R</span><span class="stringliteral">"(",</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="stringliteral"> "stride_w": )" + strides+ R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="stringliteral"> "stride_h": )" + strides+ R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="stringliteral"> "depth_multiplier": 1,</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> <span class="stringliteral"> "fused_activation_function": "NONE"</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <span class="stringliteral"> },</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> <span class="stringliteral"> "custom_options_format": "FLEXBUFFERS"</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> <span class="stringliteral"> ],</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> <span class="stringliteral"> } ],</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <span class="stringliteral"> "buffers" : [</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <span class="stringliteral"> { },</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> <span class="stringliteral"> { },</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> <span class="stringliteral"> { "data": )" + filterData + R</span><span class="stringliteral">"(, }, )"</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> <span class="stringliteral"> + biasBuffer + R</span><span class="stringliteral">"(</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="stringliteral"> ]</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="stringliteral"> )";</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="stringliteral"> <a class="code" href="struct_parser_flatbuffers_fixture.xhtml#a2bb4ea256fbbf6d53068ca93bb4bc95c">SetupSingleInputSingleOutput</a>(</span><span class="stringliteral">"inputTensor"</span>, <span class="stringliteral">"outputTensor"</span>);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  }</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> };</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> <span class="keyword">struct </span>DepthwiseConvolution2dSameFixture : DepthwiseConvolution2dFixture</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> {</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  DepthwiseConvolution2dSameFixture()</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  : DepthwiseConvolution2dFixture(<span class="stringliteral">"[ 1, 3, 3, 1 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="stringliteral">"[ 1, 3, 3, 1 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="stringliteral">"[ 1, 3, 3, 1 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="stringliteral">"[ 9,8,7, 6,5,4, 3,2,1 ]"</span>, <span class="comment">// filterData</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="stringliteral">"SAME"</span>) <span class="comment">// padding type</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  {}</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> };</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dSameFixture, <span class="stringliteral">"ParseDepthwiseConv2DSame"</span>)</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> {</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  RunTest<4, armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  0,</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  { 0, 1, 2,</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  3, 4, 5,</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  6, 7, 8 },</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// the expected values were generated using the example python implementation at</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="comment">// https://eli.thegreenplace.net/2018/depthwise-separable-convolutions-for-machine-learning/</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="comment">// divide the expected values by the output scale, as it is not 1.0</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  { 14/2, 35/2, 38/2,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  57/2, 120/2, 111/2,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  110/2, 197/2, 158/2 });</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> }</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="keyword">struct </span>DepthwiseConvolution2dValidFixture : DepthwiseConvolution2dFixture</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> {</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  DepthwiseConvolution2dValidFixture ()</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  : DepthwiseConvolution2dFixture(<span class="stringliteral">"[ 1, 3, 3, 1 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="stringliteral">"[ 1, 1, 1, 1 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="stringliteral">"[ 1, 3, 3, 1 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="stringliteral">"[ 9,8,7, 6,5,4, 3,2,1 ]"</span>, <span class="comment">// filterData</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="stringliteral">"VALID"</span>) <span class="comment">// padding type</span></div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  {}</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> };</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> </div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dValidFixture, <span class="stringliteral">"ParseDepthwiseConv2DValid"</span>)</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> {</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  RunTest<4, armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  0,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  { 0, 1, 2,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  3, 4, 5,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  6, 7, 8 },</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="comment">// divide the expected values by the output scale, as it is not 1.0</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  { 120/2 });</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> }</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> <span class="keyword">struct </span>DepthwiseConvolution2dSameBiasFixture : DepthwiseConvolution2dFixture</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> {</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  DepthwiseConvolution2dSameBiasFixture()</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  : DepthwiseConvolution2dFixture(<span class="stringliteral">"[ 1, 3, 3, 1 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="stringliteral">"[ 1, 3, 3, 1 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="stringliteral">"[ 1, 3, 3, 1 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="stringliteral">"[ 9,8,7, 6,5,4, 3,2,1 ]"</span>, <span class="comment">// filterData</span></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="stringliteral">"[ 1 ]"</span>, <span class="comment">// biasShape</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="stringliteral">"[ 10, 0, 0, 0 ]"</span>) <span class="comment">// biasData</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  {}</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> };</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dSameBiasFixture, <span class="stringliteral">"ParseDepthwiseConv2DSameBias"</span>)</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  RunTest<4, armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  0,</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  { 0, 1, 2,</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  3, 4, 5,</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  6, 7, 8 },</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="comment">// divide the expected values by the output scale, as it is not 1.0</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  { ( 14+10)/2, ( 35+10)/2, ( 38+10)/2,</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  ( 57+10)/2, (120+10)/2, (111+10)/2,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  (110+10)/2, (197+10)/2, (158+10)/2 });</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> }</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> <span class="keyword">struct </span>DynamicDepthwiseConvolution2dSameBiasFixture : DepthwiseConvolution2dFixture</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> {</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  DynamicDepthwiseConvolution2dSameBiasFixture()</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  : DepthwiseConvolution2dFixture(<span class="stringliteral">"[ 1, 3, 3, 1 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="stringliteral">"[ ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="stringliteral">"[ 1, 3, 3, 1 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="stringliteral">"[ 9,8,7, 6,5,4, 3,2,1 ]"</span>, <span class="comment">// filterData</span></div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="stringliteral">"[ 1 ]"</span>, <span class="comment">// biasShape</span></div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="stringliteral">"[ 10, 0, 0, 0 ]"</span>) <span class="comment">// biasData</span></div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  {}</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> };</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DynamicDepthwiseConvolution2dSameBiasFixture, <span class="stringliteral">"ParseDynamicDepthwiseConv2DSameBias"</span>)</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> {</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  RunTest<4, armnn::DataType::QAsymmU8, armnn::DataType::QAsymmU8>(0,</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  { { <span class="stringliteral">"inputTensor"</span>, { 0, 1, 2,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  3, 4, 5,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  6, 7, 8 } } },</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  { { <span class="stringliteral">"outputTensor"</span>, { ( 14+10)/2, ( 35+10)/2, ( 38+10)/2,</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  ( 57+10)/2, (120+10)/2, (111+10)/2,</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  (110+10)/2, (197+10)/2, (158+10)/2 } } },</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keyword">true</span>);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> }</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> </div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> <span class="keyword">struct </span>DepthwiseConvolution2dFixture2 : <span class="keyword">public</span> <a class="code" href="struct_parser_flatbuffers_fixture.xhtml">ParserFlatbuffersFixture</a></div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> {</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keyword">explicit</span> DepthwiseConvolution2dFixture2(<span class="keyword">const</span> std::string& inputShape,</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keyword">const</span> std::string& outputShape,</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keyword">const</span> std::string& filterShape,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keyword">const</span> std::string& filterData,</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keyword">const</span> std::string& strides,</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keyword">const</span> std::string& paddingType,</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keyword">const</span> std::string biasShape = <span class="stringliteral">""</span>,</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keyword">const</span> std::string biasData = <span class="stringliteral">""</span>,</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keyword">const</span> std::string filter_quant_min = <span class="stringliteral">"[ 0.0 ]"</span>,</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">const</span> std::string filter_quant_max = <span class="stringliteral">"[ 255.0 ]"</span>,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keyword">const</span> std::string filter_quant_scale = <span class="stringliteral">"[ 1.0 ]"</span>,</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keyword">const</span> std::string filter_quant_zero_point = <span class="stringliteral">"[ 0 ]"</span>,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keyword">const</span> std::string filter_quant_axis = <span class="stringliteral">""</span>,</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keyword">const</span> std::string output_scale = <span class="stringliteral">"[ 1.0 ]"</span>)</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  {</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  std::string inputTensors = <span class="stringliteral">"[ 0, 2 ]"</span>;</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  std::string biasTensor = <span class="stringliteral">""</span>;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  std::string biasBuffer = <span class="stringliteral">""</span>;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">if</span> (biasShape.size() > 0 && biasData.size() > 0)</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  {</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  inputTensors = <span class="stringliteral">"[ 0, 2, 3 ]"</span>;</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  biasTensor = R<span class="stringliteral">"(</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <span class="stringliteral"> {</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> <span class="stringliteral"> "shape": )" + biasShape + R</span><span class="stringliteral">"( ,</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> <span class="stringliteral"> "type": "INT32",</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> <span class="stringliteral"> "buffer": 3,</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> <span class="stringliteral"> "name": "biasTensor",</span></div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> <span class="stringliteral"> "quantization": {</span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="stringliteral"> "min": [ 0.0 ],</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="stringliteral"> "max": [ 255.0 ],</span></div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span> <span class="stringliteral"> "scale": [ 1.0 ],</span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> <span class="stringliteral"> "zero_point": [ 0 ],</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> <span class="stringliteral"> } )";</span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> <span class="stringliteral"> biasBuffer = R</span><span class="stringliteral">"(</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> <span class="stringliteral"> { "data": )" + biasData + R</span><span class="stringliteral">"(, }, )";</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> <span class="stringliteral"></span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> <span class="stringliteral"> std::string filter_qantization =</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> <span class="stringliteral"> R</span><span class="stringliteral">"(</span></div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> <span class="stringliteral"> "min": )" + filter_quant_min + R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> <span class="stringliteral"> "max": )" + filter_quant_max + R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> <span class="stringliteral"> "scale": )" + filter_quant_scale + R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <span class="stringliteral"> "zero_point": )" + filter_quant_zero_point;</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> <span class="stringliteral"> </span><span class="comment">// A given quantization axis indicates if per channel quantization is used for filters</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keywordflow">if</span> (filter_quant_axis.size() > 0)</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  {</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  filter_qantization +=</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  R<span class="stringliteral">"(,</span></div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> <span class="stringliteral"> "quantized_dimension": )" + filter_quant_axis;</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> <span class="stringliteral"> m_JsonString = R</span><span class="stringliteral">"(</span></div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> <span class="stringliteral"> {</span></div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> <span class="stringliteral"> "version": 3,</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> <span class="stringliteral"> "operator_codes": [ { "builtin_code": "DEPTHWISE_CONV_2D" } ],</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> <span class="stringliteral"> "subgraphs": [ {</span></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> <span class="stringliteral"> "tensors": [</span></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> <span class="stringliteral"> {</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> <span class="stringliteral"> "shape": )" + inputShape + R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> <span class="stringliteral"> "type": "INT8",</span></div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> <span class="stringliteral"> "buffer": 0,</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> <span class="stringliteral"> "name": "inputTensor",</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> <span class="stringliteral"> "quantization": {</span></div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> <span class="stringliteral"> "min": [ 0.0 ],</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> <span class="stringliteral"> "max": [ 255.0 ],</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> <span class="stringliteral"> "scale": [ 1.0 ],</span></div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> <span class="stringliteral"> "zero_point": [ 0 ],</span></div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> <span class="stringliteral"> },</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> <span class="stringliteral"> {</span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> <span class="stringliteral"> "shape": )" + outputShape + R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span> <span class="stringliteral"> "type": "INT8",</span></div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span> <span class="stringliteral"> "buffer": 1,</span></div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> <span class="stringliteral"> "name": "outputTensor",</span></div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> <span class="stringliteral"> "quantization": {</span></div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> <span class="stringliteral"> "min": [ 0.0 ],</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> <span class="stringliteral"> "max": [ 511.0 ],</span></div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> <span class="stringliteral"> "scale": )" + output_scale + R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> <span class="stringliteral"> "zero_point": [ 0 ],</span></div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> <span class="stringliteral"> },</span></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> <span class="stringliteral"> {</span></div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> <span class="stringliteral"> "shape": )" + filterShape + R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> <span class="stringliteral"> "type": "INT8",</span></div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span> <span class="stringliteral"> "buffer": 2,</span></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> <span class="stringliteral"> "name": "filterTensor",</span></div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span> <span class="stringliteral"> "quantization": {)" + filter_qantization + R</span><span class="stringliteral">"(</span></div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span> <span class="stringliteral"> }, )" + biasTensor + R</span><span class="stringliteral">"(</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span> <span class="stringliteral"> ],</span></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span> <span class="stringliteral"> "inputs": [ 0 ],</span></div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> <span class="stringliteral"> "outputs": [ 1 ],</span></div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> <span class="stringliteral"> "operators": [</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> <span class="stringliteral"> {</span></div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span> <span class="stringliteral"> "opcode_index": 0,</span></div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> <span class="stringliteral"> "inputs": )" + inputTensors + R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> <span class="stringliteral"> "outputs": [ 1 ],</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> <span class="stringliteral"> "builtin_options_type": "DepthwiseConv2DOptions",</span></div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> <span class="stringliteral"> "builtin_options": {</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> <span class="stringliteral"> "padding": ")" + paddingType + R</span><span class="stringliteral">"(",</span></div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> <span class="stringliteral"> "stride_w": )" + strides+ R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> <span class="stringliteral"> "stride_h": )" + strides+ R</span><span class="stringliteral">"(,</span></div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> <span class="stringliteral"> "depth_multiplier": 1,</span></div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> <span class="stringliteral"> "fused_activation_function": "NONE"</span></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span> <span class="stringliteral"> },</span></div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> <span class="stringliteral"> "custom_options_format": "FLEXBUFFERS"</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> <span class="stringliteral"> ],</span></div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> <span class="stringliteral"> } ],</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span> <span class="stringliteral"> "buffers" : [</span></div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span> <span class="stringliteral"> { },</span></div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> <span class="stringliteral"> { },</span></div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> <span class="stringliteral"> { "data": )" + filterData + R</span><span class="stringliteral">"(, }, )"</span></div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> <span class="stringliteral"> + biasBuffer + R</span><span class="stringliteral">"(</span></div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> <span class="stringliteral"> ]</span></div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> <span class="stringliteral"> }</span></div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> <span class="stringliteral"> )";</span></div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> <span class="stringliteral"> SetupSingleInputSingleOutput(</span><span class="stringliteral">"inputTensor"</span>, <span class="stringliteral">"outputTensor"</span>);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  }</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span> };</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span> </div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> <span class="comment">// No quantization meaning scale=1.0 and offset=0.0 and tensor quantization</span></div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span> <span class="keyword">struct </span>DepthwiseConvolution2dNoQuantFixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span> {</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  DepthwiseConvolution2dNoQuantFixture()</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="stringliteral">"[ 9,8,7, 6,5,4, 3,2,1, "</span></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="stringliteral">"9,8,7, 6,5,4, 3,2,1, "</span></div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="stringliteral">"9,8,7, 6,5,4, 3,2,1 ]"</span>, <span class="comment">// filterData</span></div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="stringliteral">""</span> <span class="comment">// bias data</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  )</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  {}</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> };</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> </div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span> <span class="comment">// No quantization meaning scale=1.0 and offset=0.0 and tensor quantization</span></div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dNoQuantFixture, <span class="stringliteral">"ParseDepthwiseConv2DNoQuant"</span>)</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> {</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  0,</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45,</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22});</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span> }</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> </div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> <span class="comment">// Uses per channel quantization on weights but with scales = 1.0 and offsets = 0.0</span></div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span> <span class="keyword">struct </span>DepthwiseConvolution2dNoChannelQuantFixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> {</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  DepthwiseConvolution2dNoChannelQuantFixture()</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="stringliteral">"[ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ]"</span>, <span class="comment">//filterData</span></div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="stringliteral">"[ 1.0, 1.0, 1.0]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="stringliteral">"[ 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="stringliteral">"3"</span> <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  )</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  {}</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span> };</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span> </div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span> <span class="comment">// Uses per channel quantization on weights but with scales = 1.0 and offsets = 0.0</span></div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dNoChannelQuantFixture, <span class="stringliteral">"ParseDepthwiseConv2DFilterNoChannelQuant"</span>)</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> {</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  0,</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45,</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22});</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> }</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span> <span class="comment">// Uses per channel quantization on weights but all scales are set to the same value</span></div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span> <span class="keyword">struct </span>DepthwiseConvolution2dWeightsPerChannelQuantFixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span> {</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  DepthwiseConvolution2dWeightsPerChannelQuantFixture()</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="comment">// filterData is [ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ]</span></div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="comment">// quantized per channel with q_dim=3</span></div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="stringliteral">"[36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, "</span></div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="stringliteral">"20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 8, 4]"</span>,</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="stringliteral">"[ 0.25, 0.25, 0.25]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="stringliteral">"[ 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="stringliteral">"3"</span> <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  )</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  {}</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span> };</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span> </div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> <span class="comment">// Weights are per channel quantized but all scales are set to the same value</span></div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dWeightsPerChannelQuantFixture,</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="stringliteral">"ParseDepthwiseConv2DFilterWeightsPerChannelQuant"</span>)</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> {</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  0,</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45,</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22});</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span> }</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span> <span class="comment">// Uses per channel quantization on weights all scales are different in this test</span></div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> <span class="keyword">struct </span>DepthwiseConvolution2dWeightsPerChannelQuant1Fixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> {</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  DepthwiseConvolution2dWeightsPerChannelQuant1Fixture()</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="comment">// filterData is [ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ]</span></div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="comment">// quantized per channel with q_dim=3</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="stringliteral">"[36, 40, 70, 24, 25, 40, 12, 10, 10, 36, 40, 70, 24, "</span></div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="stringliteral">"25, 40, 12, 10, 10, 36, 40, 70, 24, 25, 40, 12, 10, 10]"</span>,</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <span class="stringliteral">"[ 0.25, 0.2, 0.1]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="stringliteral">"[ 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="stringliteral">"3"</span> <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  )</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  {}</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> };</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span> </div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span> <span class="comment">// Uses per channel quantization on weights all scales are different in this test</span></div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dWeightsPerChannelQuant1Fixture,</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="stringliteral">"ParseDepthwiseConv2DFilterWeightsPerChannelQuant1"</span>)</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> {</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  0,</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45,</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22});</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> }</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> </div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> </div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span> <span class="comment">// Uses per channel quantization on weights all scales are different in this test</span></div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span> <span class="comment">// Uses different shape for weights and input compared to the other tests above</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> <span class="keyword">struct </span>DepthwiseConvolution2dWeightsPerChannelQuant2Fixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> {</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  DepthwiseConvolution2dWeightsPerChannelQuant2Fixture()</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 4, 4, 4 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="stringliteral">"[ 1, 4, 4, 4 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="stringliteral">"[ 1, 2, 2, 4 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="comment">// filterData is [ 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3 ]</span></div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="comment">// quantized per channel with q_dim=3</span></div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="stringliteral">"[36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10]"</span>,</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="stringliteral">"[ 0.25, 0.2, 0.1, 0.3]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="stringliteral">"[ 0, 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <span class="stringliteral">"3"</span> <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  )</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  {}</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span> };</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> </div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span> <span class="comment">// Uses per channel quantization on weights all scales are different in this test</span></div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> <span class="comment">// Uses different shape for weights and input compared to the other tests above</span></div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dWeightsPerChannelQuant2Fixture,</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <span class="stringliteral">"ParseDepthwiseConv2DFilterWeightsPerChannelQuant2"</span>)</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span> {</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  0,</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1},</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  { 21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13,</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13,</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13,</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  14, 12, 10, 8, 14, 12, 10, 8, 14, 12, 10, 8, 9, 8, 7, 6});</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> }</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> </div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span> <span class="comment">// Test for depthwise_multiplier different to one (M > 1)</span></div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span> <span class="keyword">struct </span>DepthwiseConvolution2dWeightsPerChannelQuant4Fixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span> {</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  DepthwiseConvolution2dWeightsPerChannelQuant4Fixture()</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 4, 4, 4 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  <span class="stringliteral">"[ 1, 4, 4, 16 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <span class="stringliteral">"[ 1, 2, 2, 16 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <span class="comment">// filter data is [ 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3,</span></div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <span class="comment">// 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3,</span></div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  <span class="comment">// 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3,</span></div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <span class="comment">// 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3 ]</span></div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="comment">// quantized per channel with q_dim=3</span></div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="stringliteral">"[36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, "</span></div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="stringliteral">"36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, "</span></div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="stringliteral">"36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, "</span></div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="stringliteral">"36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10]"</span>,</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="stringliteral">"[ 0.25, 0.2, 0.1, 0.3,"</span></div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="stringliteral">"0.25, 0.2, 0.1, 0.3,"</span></div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="stringliteral">"0.25, 0.2, 0.1, 0.3,"</span></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <span class="stringliteral">"0.25, 0.2, 0.1, 0.3]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="stringliteral">"[ 0, 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="stringliteral">"3"</span> <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  )</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  {}</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span> };</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> </div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> <span class="comment">// Test for depthwise_multiplier different to one (M > 1)</span></div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dWeightsPerChannelQuant4Fixture,</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <span class="stringliteral">"ParseDepthwiseConv2DFilterWeightsPerChannelQuant4"</span>)</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span> {</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  0,</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1},</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  { 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  9, 8, 7, 6, 5, 4, 3, 2, 1, 9, 8, 7, 6, 5, 4, 3});</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span> }</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> </div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> </div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span> <span class="keyword">struct </span>DepthwiseConvolution2dWeightsPerChannelQuant6Fixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span> {</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  DepthwiseConvolution2dWeightsPerChannelQuant6Fixture()</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 4, 4, 4 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <span class="stringliteral">"[ 1, 4, 4, 16 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="stringliteral">"[ 1, 2, 2, 16 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <span class="comment">// filter data is [ 3,4,1,1,1,3,3,2,1,4,3,4,1,2,2,4,</span></div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <span class="comment">// 2,0,3,1,0,2,4,3,4,3,0,1,3,4,4,1,</span></div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <span class="comment">// 3,3,2,0,0,0,1,3,3,2,4,4,3,1,1,3,</span></div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <span class="comment">// 1,0,0,2,3,0,1,1,4,2,2,1,2,3,2,0]</span></div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <span class="comment">// quantized per channel with q_dim=3</span></div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="stringliteral">"[12,20,10, 3, 4,15,30, 6, 4,20,30,12, 4,10,20,12,"</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <span class="stringliteral">" 8, 0,30, 3, 0,10,40, 9,16,15, 0, 3,12,20,40, 3,"</span></div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <span class="stringliteral">" 12,15,20, 0, 0, 0,10, 9,12,10,40,12,12, 5,10, 9,"</span></div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <span class="stringliteral">" 4, 0, 0, 6,12, 0,10, 3,16,10,20, 3, 8,15,20, 0]"</span>,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <span class="stringliteral">"[ 0.25, 0.2, 0.1, 0.333333333,"</span></div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  <span class="stringliteral">"0.25, 0.2, 0.1, 0.333333333,"</span></div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="stringliteral">"0.25, 0.2, 0.1, 0.333333333,"</span></div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="stringliteral">"0.25, 0.2, 0.1, 0.333333333]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="stringliteral">"[ 0, 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="stringliteral">"3"</span> <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  )</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  {}</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span> };</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span> </div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dWeightsPerChannelQuant6Fixture,</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="stringliteral">"ParseDepthwiseConv2DFilterWeightsPerChannelQuant6"</span>)</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span> {</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  0,</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  { 1,0,1,2,0,4,4,0,2,1,2,0,1,3,3,0,</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  1,2,2,3,3,4,1,1,2,4,1,3,4,2,0,2,</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  0,3,1,3,4,3,2,0,1,2,3,3,0,2,4,2,</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  1,2,1,4,3,4,1,3,1,0,2,3,1,3,2,0},</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  { 9, 7, 3, 7,12, 8,22,22,27,22,13,17,13,10, 9,17,</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  15, 9,12, 6,16,14,24,27,19,26,18,23, 9,10, 7, 3,</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  18,14, 9,11, 7, 9,21,25,17,19,10,15,13, 9, 7, 9,</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  15,16, 9, 1, 3, 9,11,12, 3,12, 9,12, 6, 2, 2, 6,</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  13, 4,10,12,11,14,28,28,17,17,14,15,15,13,13,22,</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  26,24,17, 7,10,20,33,31,23,17,17,16,16,23,20, 7,</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  17,11,16, 6,10,16,24,22,26,18,23,20,22,23,21,23,</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  12,16, 4, 4, 2, 6, 8,10,12, 8,16,16, 8, 6, 6,14,</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  14, 3,14,10,15,15,27,25,16,14, 9,11,21,19,16,24,</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  24,25,13, 7, 3,13,21,24,25,23,14,17,24,24,21,12,</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  7, 7, 3, 3,11,10,17,13,33,32,21,26,18,17,17,23,</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  3, 3, 2, 0, 2, 6, 9,13,10,20,20,24, 2, 4, 4, 8,</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  9, 4,10, 4, 2,14,22,16, 5, 7, 3, 5,13,20,20,19,</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  11,12, 6, 4, 4,12,12, 8, 9,10, 3, 6,12,18,18,15,</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  5, 4, 4, 2, 0, 6,12, 9,10,14, 6,10, 3, 6, 6,12,</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  3, 4, 1, 1, 3, 9, 9, 6, 2, 8, 6, 8, 0, 0, 0, 0});</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span> }</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span> </div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span> </div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span> <span class="keyword">struct </span>DepthwiseConvolution2dWeightsPerChannelQuant1_1Fixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span> {</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  DepthwiseConvolution2dWeightsPerChannelQuant1_1Fixture()</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  <span class="comment">// filterData is [ 1,4,0,2,4,3,1,0,1,</span></div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <span class="comment">// 3,0,4,0,1,3,4,2,4,</span></div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <span class="comment">// 3,0,3,4,4,0,3,4,2]</span></div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <span class="comment">// quantized per channel with q_dim=3</span></div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="stringliteral">"[ 4,20, 0, 8,20,30, 4, 0,10,12,"</span></div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <span class="stringliteral">" 0,40, 0, 5,30,16,10,40,12, 0,"</span></div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <span class="stringliteral">"30,16,20, 0,12,20,20]"</span>,</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <span class="stringliteral">"[ 0.25, 0.2, 0.1]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <span class="stringliteral">"[ 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="stringliteral">"3"</span> <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  )</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  {}</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span> };</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span> </div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span> </div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dWeightsPerChannelQuant1_1Fixture,</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="stringliteral">"ParseDepthwiseConv2DFilterWeightsPerChannelQuant1_1"</span>)</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span> {</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  0,</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  { 11,11, 9,17,11,16,10, 5,10,</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  14,15,13,21,19,20,13,13,13,</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  7, 7,11,11,11,15, 6, 9,10});</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span> }</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span> </div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span> <span class="comment">// Same with input different to 1</span></div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span> <span class="keyword">struct </span>DepthwiseConvolution2dWeightsPerChannelQuant1_2Fixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span> {</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  DepthwiseConvolution2dWeightsPerChannelQuant1_2Fixture()</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  <span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <span class="stringliteral">"[ 1, 3, 3, 3 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <span class="comment">// filterData is [ 1,4,0,2,4,3,1,0,1,</span></div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  <span class="comment">// 3,0,4,0,1,3,4,2,4,</span></div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <span class="comment">// 3,0,3,4,4,0,3,4,2]</span></div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="comment">// quantized per channel with q_dim=3</span></div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <span class="stringliteral">"[ 4,20, 0, 8,20,30, 4, 0,10,12,"</span></div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <span class="stringliteral">" 0,40, 0, 5,30,16,10,40,12, 0,"</span></div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <span class="stringliteral">"30,16,20, 0,12,20,20]"</span>,</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <span class="stringliteral">"[ 0.25, 0.2, 0.1]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <span class="stringliteral">"[ 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="stringliteral">"3"</span> <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  )</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  {}</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span> };</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span> </div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span> </div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dWeightsPerChannelQuant1_2Fixture,</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <span class="stringliteral">"ParseDepthwiseConv2DFilterWeightsPerChannelQuant1_2"</span>)</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span> {</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  0,</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  { 3,2,0,0,4,3,0,1,2,</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  0,1,3,0,4,2,2,2,3,</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  2,4,3,2,0,4,3,4,0},</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  { 0,30,16,15,30,32, 8, 9,24,</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  20,33,28,34,48,50,18,38,35,</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  8, 8,36,20,28,33,10,28,25});</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span> }</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span> </div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span> </div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span> <span class="keyword">struct </span>DepthwiseConvolution2dWeightsPerChannelQuant4_1Fixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span> {</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  DepthwiseConvolution2dWeightsPerChannelQuant4_1Fixture()</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 4, 4, 4 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <span class="stringliteral">"[ 1, 4, 4, 16 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <span class="stringliteral">"[ 1, 2, 2, 16 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <span class="comment">// filter data is [ 3,4,1,1,1,3,3,2,1,4,3,4,1,2,2,4,</span></div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  <span class="comment">// 2,0,3,1,0,2,4,3,4,3,0,1,3,4,4,1,</span></div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <span class="comment">// 3,3,2,0,0,0,1,3,3,2,4,4,3,1,1,3,</span></div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <span class="comment">// 1,0,0,2,3,0,1,1,4,2,2,1,2,3,2,0 ]</span></div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <span class="comment">// quantized per channel with q_dim=3</span></div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <span class="stringliteral">"[12,20,10, 3, 4,15,30, 6, 4,20,30,13, 4,10,20,13,"</span></div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <span class="stringliteral">" 8, 0,30, 3, 0,10,40,10,16,15, 0, 3,12,20,40, 3,"</span></div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  <span class="stringliteral">" 12,15,20, 0, 0, 0,10,10,12,10,40,13,12, 5,10,10,"</span></div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <span class="stringliteral">" 4, 0, 0, 6,12, 0,10, 3,16,10,20, 3, 8,15,20, 0]"</span>,</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <span class="stringliteral">"[ 0.25, 0.2, 0.1, 0.3,"</span></div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <span class="stringliteral">"0.25, 0.2, 0.1, 0.3,"</span></div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  <span class="stringliteral">"0.25, 0.2, 0.1, 0.3,"</span></div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <span class="stringliteral">"0.25, 0.2, 0.1, 0.3]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="stringliteral">"[ 0, 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="stringliteral">"3"</span> <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  )</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  {}</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span> };</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span> </div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span> </div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dWeightsPerChannelQuant4_1Fixture,</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <span class="stringliteral">"ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_1"</span>)</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span> {</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  0,</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1},</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  { 9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  6, 7, 3, 1, 1, 3, 4, 5, 4, 6, 7, 8, 4, 3, 3, 7,</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  6, 7, 3, 1, 1, 3, 4, 5, 4, 6, 7, 8, 4, 3, 3, 7,</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  6, 7, 3, 1, 1, 3, 4, 5, 4, 6, 7, 8, 4, 3, 3, 7,</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  5, 4, 4, 2, 1, 5, 7, 5, 5, 7, 3, 5, 4, 6, 6, 5,</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  5, 4, 4, 2, 1, 5, 7, 5, 5, 7, 3, 5, 4, 6, 6, 5,</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  5, 4, 4, 2, 1, 5, 7, 5, 5, 7, 3, 5, 4, 6, 6, 5,</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  3, 4, 1, 1, 1, 3, 3, 2, 1, 4, 3, 4, 1, 2, 2, 4});</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span> }</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span> </div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span> </div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span> </div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span> <span class="keyword">struct </span>DepthwiseConvolution2dWeightsPerChannelQuant4_2Fixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span> {</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  DepthwiseConvolution2dWeightsPerChannelQuant4_2Fixture()</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 4, 4, 4 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <span class="stringliteral">"[ 1, 4, 4, 16 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  <span class="stringliteral">"[ 1, 2, 2, 16 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <span class="comment">// filter data is [ 3,4,1,1,1,3,3,2,1,4,3,4,1,2,2,4,</span></div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <span class="comment">// 2,0,3,1,0,2,4,3,4,3,0,1,3,4,4,1,</span></div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <span class="comment">// 3,3,2,0,0,0,1,3,3,2,4,4,3,1,1,3,</span></div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <span class="comment">// 1,0,0,2,3,0,1,1,4,2,2,1,2,3,2,0 ]</span></div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <span class="comment">// quantized per channel with q_dim=3</span></div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  <span class="stringliteral">"[12,20,10, 3, 4,15,30, 6, 4,20,30,13, 4,10,20,13,"</span></div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  <span class="stringliteral">" 8, 0,30, 3, 0,10,40,10,16,15, 0, 3,12,20,40, 3,"</span></div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  <span class="stringliteral">" 12,15,20, 0, 0, 0,10,10,12,10,40,13,12, 5,10,10,"</span></div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <span class="stringliteral">" 4, 0, 0, 6,12, 0,10, 3,16,10,20, 3, 8,15,20, 0]"</span>,</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <span class="stringliteral">"[ 0.25, 0.2, 0.1, 0.3,"</span></div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  <span class="stringliteral">"0.25, 0.2, 0.1, 0.3,"</span></div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  <span class="stringliteral">"0.25, 0.2, 0.1, 0.3,"</span></div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <span class="stringliteral">"0.25, 0.2, 0.1, 0.3]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  <span class="stringliteral">"[ 0, 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  <span class="stringliteral">"3"</span> <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  )</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  {}</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span> };</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span> </div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span> </div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dWeightsPerChannelQuant4_2Fixture,</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  <span class="stringliteral">"ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_2"</span>)</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span> {</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  0,</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  { 3,3,3,4, 4,4,0,0, 0,3,4,3, 0,2,2,3,</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  3,0,3,0, 0,3,2,1, 4,1,2,2, 0,0,0,4,</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  3,2,2,2, 2,1,0,4, 4,3,2,4, 3,2,0,0,</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  4,1,4,4, 1,0,4,3, 3,2,0,3, 1,1,0,2},</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  { 26,21,21, 7,12,17,28,21,20,22,25,26, 6,11,10,16,</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  16,16, 4,12, 7,18,28,27,30,20,12,14,16,19,17, 6,</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  12,12, 8, 0, 3,13,18,15,18,26,20,26,26,32,28,21,</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  0, 0, 0, 0, 2, 6, 6, 4, 2, 8, 6, 8,15,10,10,24,</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  20,21, 9, 7, 3, 6,15,16,17,22,17,22,17,18,14, 7,</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  18, 6,16,12,12,11,17,15,18,18,10,12,27,26,22,18,</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  27,28,12,10, 7, 3, 8,13, 8,12,14,16,26,24,24,24,</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  9, 9, 6, 0, 0, 0, 2, 6, 0, 0, 0, 0, 4, 8, 8,16,</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  26,24,17, 7, 2, 8,11,10,30,24,30,28,32,33,30,24,</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  20,11,16,12, 7, 9,17,13,20,14,16,18,31,36,33,29,</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  28,25,19, 9, 6,13,20,19, 2, 8, 6, 8,17,17,15,25,</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  12,15, 5, 3, 2, 6, 7, 7, 0, 0, 0, 0, 6, 2, 2, 6,</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  14,16, 7, 5, 1, 3, 3, 2,20,28,12,20,13,20,20,19,</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  9, 4,10, 4, 0, 4, 8, 6, 4,16,12,16,12,18,18,15,</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  11,12, 6, 4, 2, 8,10, 7, 0, 0, 0, 0, 9,14,14,14,</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  3, 4, 1, 1, 1, 3, 3, 2, 0, 0, 0, 0, 2, 4, 4, 8});</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span> }</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span> </div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span> </div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span> <span class="keyword">struct </span>DepthwiseConvolution2dWeightsPerChannelQuant4_5Fixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span> {</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  DepthwiseConvolution2dWeightsPerChannelQuant4_5Fixture()</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 4, 4, 4 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  <span class="stringliteral">"[ 1, 4, 4, 16 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  <span class="stringliteral">"[ 1, 2, 2, 16 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <span class="comment">// filter data is [ 1, 4, 9, 16, 25, 36,</span></div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  <span class="comment">// 49, 64, 81, 100, 121, 144,</span></div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  <span class="comment">// 169, 196, 225, 256, 17, 36,</span></div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <span class="comment">// 57, 80, 105, 132, 161, 192,</span></div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  <span class="comment">// 225, 260, 297, 336, 377, 420,</span></div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  <span class="comment">// 465, 512, 33, 68, 105, 144,</span></div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  <span class="comment">// 185, 228, 273, 320, 369, 420,</span></div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  <span class="comment">// 473, 528, 585, 644, 705, 768,</span></div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  <span class="comment">// 49, 100, 153, 208, 265, 324,</span></div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  <span class="comment">// 385, 448, 513, 580, 649, 720,</span></div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  <span class="comment">// 793, 868, 945,1024 ]</span></div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <span class="comment">// quantized per channel with q_dim=3</span></div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  <span class="stringliteral">"[ 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15,16,"</span></div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <span class="stringliteral">" 17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,"</span></div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  <span class="stringliteral">" 33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,"</span></div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  <span class="stringliteral">"49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64]"</span>,</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  <span class="stringliteral">"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,15,16]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <span class="stringliteral">"[ 0, 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <span class="stringliteral">"3"</span>, <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  <span class="stringliteral">"[ 100.0 ]"</span> <span class="comment">// output scale</span></div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  )</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  {}</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span> };</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span> </div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span> <span class="comment">// Test for depthwise_multiplier different to one (M > 1)</span></div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dWeightsPerChannelQuant4_5Fixture,</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <span class="stringliteral">"ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_5"</span>)</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span> {</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  0,</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  { 1,1,1,2,2,2,1,2,1,2,2,1,2,2,1,1,1,1,1,1,1,2,2,2,</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  1,2,2,2,1,1,1,2,1,1,1,1,2,1,2,1,2,1,1,2,1,2,1,1,</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  1,2,2,1,2,2,1,1,2,1,2,1,1,2,1,2},</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  { 1, 2, 3, 5, 9,11,14,16,17,19,21,24,32,36,39,43,</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  1, 2, 3, 4,11,14,17,20,22,26,29,33,34,38,42,46,</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  1, 2, 3, 5, 8,11,13,16,16,18,21,24,33,36,39,43,</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  0, 0, 1, 1, 2, 3, 3, 4, 4, 5, 5, 6,13,14,16,17,</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  1, 3, 4, 6, 6, 8,10,12,19,22,24,27,23,25,28,30,</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  1, 3, 5, 8, 7, 8,10,12,18,21,24,27,32,36,39,43,</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  1, 2, 4, 5, 8,10,13,15,12,14,16,18,30,33,37,40,</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  0, 0, 1, 1, 3, 4, 5, 7, 4, 5, 5, 6, 9,10,11,12,</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  1, 3, 5, 7,10,12,15,17,17,20,23,25,19,21,23,25,</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  2, 4, 6, 8, 7, 9,11,13,17,20,23,25,23,25,28,30,</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  1, 2, 4, 6, 9,11,14,16,15,17,20,22,28,31,35,38,</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  0, 0, 1, 1, 4, 5, 6, 7, 4, 5, 5, 6,13,14,16,17,</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  0, 0, 1, 1, 2, 3, 4, 5, 3, 4, 5, 6, 5, 6, 6, 7,</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  0, 0, 1, 1, 1, 2, 2, 3, 5, 6, 7, 8, 5, 6, 6, 7,</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  0, 0, 0, 1, 2, 3, 3, 4, 3, 4, 5, 6, 9,10,11,12,</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 3, 3, 4, 5});</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span> }</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span> </div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span> </div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span> <span class="keyword">struct </span>DepthwiseConvolution2dWeightsPerChannelQuant4_3_1Fixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span> {</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  DepthwiseConvolution2dWeightsPerChannelQuant4_3_1Fixture()</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 4, 4, 4 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  <span class="stringliteral">"[ 1, 4, 4, 16 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  <span class="stringliteral">"[ 1, 2, 2, 16 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <span class="comment">// filter data is [ 3,4,1,1,1,3,3,2,1,4,3,4,1,2,2,4,</span></div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  <span class="comment">// 2,0,3,1,0,2,4,3,4,3,0,1,3,4,4,1,</span></div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  <span class="comment">// 3,3,2,0,0,0,1,3,3,2,4,4,3,1,1,3,</span></div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <span class="comment">// 1,0,0,2,3,0,1,1,4,2,2,1,2,3,2,0 ]</span></div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  <span class="comment">// quantized per channel with q_dim=3</span></div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  <span class="stringliteral">"[12,20,10, 3, 2,24, 9,10, 5,16,30,12, 3,10, 4,32,"</span></div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  <span class="stringliteral">" 8, 0,30, 3, 0,16,12,15,20,12, 0, 3, 9,20, 8, 8,"</span></div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  <span class="stringliteral">" 12,15,20, 0, 0, 0, 3,15,15, 8,40,12, 9, 5, 2,24,"</span></div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  <span class="stringliteral">" 4, 0, 0, 6, 6, 0, 3, 5,20, 8,20, 3, 6,15, 4, 0]"</span>,</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  <span class="stringliteral">"[0.25, 0.2, 0.1, 0.3333333333, "</span></div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  <span class="stringliteral">"0.5, 0.125, 0.33333333, 0.2, "</span></div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  <span class="stringliteral">"0.2, 0.25, 0.1, 0.333333333, "</span></div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  <span class="stringliteral">"0.3333333333, 0.2, 0.5, 0.125]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <span class="stringliteral">"[ 0, 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <span class="stringliteral">"3"</span> <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  )</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  {}</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span> };</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span> </div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span> <span class="comment">// Test for depthwise_multiplier different to one (M > 1)</span></div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dWeightsPerChannelQuant4_3_1Fixture,</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  <span class="stringliteral">"ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_3_1"</span>)</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span> {</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  0,</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  { 3,3,3,4, 4,4,0,0, 0,3,4,3, 0,2,2,3,</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  3,0,3,0, 0,3,2,1, 4,1,2,2, 0,0,0,4,</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  3,2,2,2, 2,1,0,4, 4,3,2,4, 3,2,0,0,</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  4,1,4,4, 1,0,4,3, 3,2,0,3, 1,1,0,2},</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  { 26,21,21, 7,12,17,28,21,20,22,25,26, 6,11,10,16,</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  16,16, 4,12, 7,18,28,27,30,20,12,14,16,19,17, 6,</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  12,12, 8, 0, 3,13,18,15,18,26,20,26,26,32,28,21,</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  0, 0, 0, 0, 2, 6, 6, 4, 2, 8, 6, 8,15,10,10,24,</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  20,21, 9, 7, 3, 6,15,16,17,22,17,22,17,18,14, 7,</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  18, 6,16,12,12,11,17,15,18,18,10,12,27,26,22,18,</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  27,28,12,10, 7, 3, 8,13, 8,12,14,16,26,24,24,24,</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  9, 9, 6, 0, 0, 0, 2, 6, 0, 0, 0, 0, 4, 8, 8,16,</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  26,24,17, 7, 2, 8,11,10,30,24,30,28,32,33,30,24,</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  20,11,16,12, 7, 9,17,13,20,14,16,18,31,36,33,29,</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  28,25,19, 9, 6,13,20,19, 2, 8, 6, 8,17,17,15,25,</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  12,15, 5, 3, 2, 6, 7, 7, 0, 0, 0, 0, 6, 2, 2, 6,</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  14,16, 7, 5, 1, 3, 3, 2,20,28,12,20,13,20,20,19,</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  9, 4,10, 4, 0, 4, 8, 6, 4,16,12,16,12,18,18,15,</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  11,12, 6, 4, 2, 8,10, 7, 0, 0, 0, 0, 9,14,14,14,</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  3, 4, 1, 1, 1, 3, 3, 2, 0, 0, 0, 0, 2, 4, 4, 8});</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span> }</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span> </div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span> <span class="keyword">struct </span>DepthwiseConvolution2dWeightsPerChannelQuant4_3_2Fixture : DepthwiseConvolution2dFixture2</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span> {</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  DepthwiseConvolution2dWeightsPerChannelQuant4_3_2Fixture()</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  : DepthwiseConvolution2dFixture2(<span class="stringliteral">"[ 1, 2, 2, 2 ]"</span>, <span class="comment">// inputShape</span></div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <span class="stringliteral">"[ 1, 2, 2, 4 ]"</span>, <span class="comment">// outputShape</span></div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  <span class="stringliteral">"[ 1, 3, 3, 4 ]"</span>, <span class="comment">// filterShape</span></div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <span class="comment">// filter data is [ 0,1,2,3,4,5,6,7,8,</span></div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <span class="comment">// 0,1,2,3,4,5,6,7,8,</span></div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  <span class="comment">// 0,1,2,3,4,5,6,7,8,</span></div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  <span class="comment">// 0,1,2,3,4,5,6,7,8 ]</span></div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <span class="comment">// quantized per channel with q_dim=3</span></div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  <span class="stringliteral">"[0, 5,20, 9,16,25,60,21,32,"</span></div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <span class="stringliteral">" 0,10, 6,12,20,50,18,28,40,"</span></div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  <span class="stringliteral">" 0, 3, 8,15,40,15,24,35,80,"</span></div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  <span class="stringliteral">" 0, 4,10,30,12,20,30,70,24]"</span>,</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  <span class="stringliteral">"1"</span>, <span class="comment">// stride w and h</span></div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  <span class="stringliteral">"SAME"</span>, <span class="comment">// padding type</span></div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  <span class="stringliteral">""</span>, <span class="comment">// bias shape</span></div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  <span class="stringliteral">""</span>, <span class="comment">// bias data</span></div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  <span class="stringliteral">"[ 0.0 ]"</span>, <span class="comment">// filter quantization min values</span></div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  <span class="stringliteral">"[ 255.0 ]"</span>, <span class="comment">// filter quantization max values</span></div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  <span class="stringliteral">"[0.25, 0.2, 0.1, 0.3333333333]"</span>, <span class="comment">// filter quantization scales</span></div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  <span class="stringliteral">"[ 0, 0, 0, 0]"</span>, <span class="comment">// filter quantization zero-points</span></div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  <span class="stringliteral">"3"</span> <span class="comment">// filter quantized axis</span></div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  <span class="comment">// (in case of per channel quantization)</span></div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  )</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  {}</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span> };</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span> </div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span> <span class="comment">// An easy test with M > 1 for debugging</span></div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span> <a class="code" href="_mem_copy_tests_8cpp.xhtml#a3df1acc0ccc35bce0bd6c027e23e2c45">TEST_CASE_FIXTURE</a>(DepthwiseConvolution2dWeightsPerChannelQuant4_3_2Fixture,</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  <span class="stringliteral">"ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_3_2"</span>)</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span> {</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  RunTest<4, armnn::DataType::QAsymmS8>(</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  0,</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  { 0,1,2,3,4,5,6,7},</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  { 38,50,76,92,44,56,66,37,56,50,37,53,62,74,45,61});</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span> }</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span> </div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span> } <span class="comment">// end of TEST_SUITE("TensorflowLiteParser_DepthwiseConvolution2D")</span></div><div class="ttc" id="_depthwise_convolution2_d_8cpp_xhtml_a75c44cab4542630baaefa446e81cbf01"><div class="ttname"><a href="_depthwise_convolution2_d_8cpp.xhtml#a75c44cab4542630baaefa446e81cbf01">TEST_SUITE</a></div><div class="ttdeci">TEST_SUITE("TensorflowLiteParser_DepthwiseConvolution2D")</div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution2_d_8cpp_source.xhtml#l00009">DepthwiseConvolution2D.cpp:9</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#l00035">ParserFlatbuffersFixture.hpp:35</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, "CopyBetweenNeonAndGpu")</div><div class="ttdef"><b>Definition:</b> <a href="_mem_copy_tests_8cpp_source.xhtml#l00089">MemCopyTests.cpp:89</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="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 &inputName, const std::string &outputName)</div><div class="ttdef"><b>Definition:</b> <a href="_parser_flatbuffers_fixture_8hpp_source.xhtml#l00152">ParserFlatbuffersFixture.hpp:152</a></div></div>
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