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<div class="title">ConvolutionLayer.cpp</div> </div>
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<a href="validation_2_c_p_p_2_convolution_layer_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment"> * Copyright (c) 2017 ARM Limited.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div>
<div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div>
<div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div>
<div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div>
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div>
<div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;<span class="comment"> * SOFTWARE.</span></div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tests_2validation_2_c_p_p_2_convolution_layer_8h.xhtml">ConvolutionLayer.h</a>&quot;</span></div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;</div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tests_2validation_2_fixed_point_8h.xhtml">tests/validation/FixedPoint.h</a>&quot;</span></div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tests_2validation_2_helpers_8h.xhtml">tests/validation/Helpers.h</a>&quot;</span></div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;</div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;<span class="keyword">namespace </span>arm_compute</div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;{</div>
<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;<span class="keyword">namespace </span>test</div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;{</div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;<span class="keyword">namespace </span>validation</div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;{</div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;<span class="keyword">namespace </span>reference</div>
<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;{</div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;<span class="keyword">namespace</span></div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;{</div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;<span class="keyword">inline</span> <span class="keywordtype">bool</span> is_valid_pixel(<span class="keywordtype">int</span> i, <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aabcf39e3917f842dbc5fbb0d802f24d5">min</a>, <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">max</a>)</div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;{</div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <span class="keywordflow">return</span> (i &gt;= min &amp;&amp; i &lt; max);</div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;}</div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160;</div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160;<span class="comment">// 3D convolution for floating point type</span></div>
<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160;<a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">template &lt;typename T, typename std::enable_if&lt;is_floating_point&lt;T&gt;::value</a>, <span class="keywordtype">int</span>&gt;<a class="code" href="namespacemali__userspace.xhtml#ad44b615021ed3ccb734fcaf583ef4a03">::type</a> = 0&gt;</div>
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160;<span class="keywordtype">void</span> convolution3d(<span class="keyword">const</span> T *in, <span class="keyword">const</span> T *weights, <span class="keyword">const</span> T *bias, T *out, <span class="keywordtype">int</span> xi, <span class="keywordtype">int</span> yi, <span class="keywordtype">int</span> width_in, <span class="keywordtype">int</span> height_in, <span class="keywordtype">int</span> depth_in, <span class="keywordtype">int</span> width_weights, <span class="keywordtype">int</span> height_weights, <span class="keywordtype">int</span> fixed_point_position)</div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;{</div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <a class="code" href="_error_8h.xhtml#a4103adbb45806b2f2002d44b91d0d206">ARM_COMPUTE_UNUSED</a>(fixed_point_position);</div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160;</div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> half_width_weights = width_weights / 2;</div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> half_height_weights = height_weights / 2;</div>
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160;</div>
<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; <span class="comment">// Reset accumulator</span></div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; T acc(0);</div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160;</div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; <span class="comment">// Compute a 2D convolution for each IFM and accumulate the result</span></div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> ifm = 0; ifm &lt; depth_in; ++ifm)</div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; {</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <span class="comment">// Compute the offset for the input slice</span></div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;</div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160;</div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; <span class="comment">// Compute 2D convolution</span></div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> yk = -half_height_weights; yk &lt;= half_height_weights; ++yk)</div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; {</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> xk = -half_width_weights; xk &lt;= half_width_weights; ++xk)</div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; {</div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <span class="comment">// Check if the pixel is out-of-bound</span></div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <span class="keywordflow">if</span>(is_valid_pixel(xi + xk, 0, width_in) &amp;&amp; is_valid_pixel(yi + yk, 0, height_in))</div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; {</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> idx = xk + half_width_weights;</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> idy = yk + half_height_weights;</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160;</div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; <span class="keyword">const</span> T i_value = in[offset_slice_in + xk + yk * width_in];</div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <span class="keyword">const</span> T w_value = weights[idx + idy * width_weights + ifm * width_weights * height_weights];</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160;</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; acc += i_value * w_value;</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; }</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; }</div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; }</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; }</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160;</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; <span class="comment">// Accumulate the bias and store the result</span></div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; *out = acc + (*bias);</div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;}</div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160;</div>
<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160;<span class="comment">// 3D convolution for fixed point type</span></div>
<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160;<a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">template &lt;typename T, typename std::enable_if&lt;std::is_integral&lt;T&gt;::value</a>, <span class="keywordtype">int</span>&gt;::type = 0&gt;</div>
<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;<span class="keywordtype">void</span> convolution3d(<span class="keyword">const</span> T *in, <span class="keyword">const</span> T *weights, <span class="keyword">const</span> T *bias, T *out, <span class="keywordtype">int</span> xi, <span class="keywordtype">int</span> yi, <span class="keywordtype">int</span> width_in, <span class="keywordtype">int</span> height_in, <span class="keywordtype">int</span> depth_in, <span class="keywordtype">int</span> width_weights, <span class="keywordtype">int</span> height_weights,</div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; <span class="keywordtype">int</span> fixed_point_position)</div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160;{</div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> half_width_weights = width_weights / 2;</div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> half_height_weights = height_weights / 2;</div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160;</div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; <span class="keyword">using namespace </span>fixed_point_arithmetic;</div>
<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; <span class="keyword">using</span> promoted_type = fixed_point_arithmetic::traits::promote_t&lt;T&gt;;</div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160;</div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; <span class="comment">// Reset accumulator</span></div>
<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; fixed_point&lt;promoted_type&gt; acc(0, fixed_point_position);</div>
<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160;</div>
<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; <span class="comment">// Compute a 2D convolution for each IFM and accumulate the result</span></div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> ifm = 0; ifm &lt; depth_in; ++ifm)</div>
<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; {</div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; <span class="comment">// Compute the offset for the input slice</span></div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;</div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160;</div>
<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; <span class="comment">// Compute 2D convolution</span></div>
<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> yk = -half_height_weights; yk &lt;= half_height_weights; ++yk)</div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; {</div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> xk = -half_width_weights; xk &lt;= half_width_weights; ++xk)</div>
<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; {</div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <span class="comment">// Check if the pixel is out-of-bound</span></div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <span class="keywordflow">if</span>(is_valid_pixel(xi + xk, 0, width_in) &amp;&amp; is_valid_pixel(yi + yk, 0, height_in))</div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; {</div>
<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> idx = xk + half_width_weights;</div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> idy = yk + half_height_weights;</div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160;</div>
<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; <span class="keyword">const</span> fixed_point&lt;promoted_type&gt; i_value(in[offset_slice_in + xk + yk * width_in], fixed_point_position, <span class="keyword">true</span>);</div>
<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; <span class="keyword">const</span> fixed_point&lt;promoted_type&gt; w_value(weights[idx + idy * width_weights + ifm * width_weights * height_weights], fixed_point_position, <span class="keyword">true</span>);</div>
<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; <span class="keyword">const</span> fixed_point&lt;promoted_type&gt; iw = i_value * w_value;</div>
<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; acc = iw + acc;</div>
<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; }</div>
<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; }</div>
<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; }</div>
<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; }</div>
<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160;</div>
<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <span class="comment">// Get the bias</span></div>
<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; <span class="keyword">const</span> fixed_point&lt;promoted_type&gt; b(*bias, fixed_point_position, <span class="keyword">true</span>);</div>
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160;</div>
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <span class="comment">// Accumulate the bias and covert back</span></div>
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; acc = acc + b;</div>
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; fixed_point&lt;T&gt; res(acc);</div>
<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; *out = res.raw();</div>
<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160;}</div>
<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160;} <span class="comment">// namespace</span></div>
<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160;</div>
<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div>
<div class="line"><a name="l00137"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aeec300028ef21b06bc60da82c894a010"> 137</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aeec300028ef21b06bc60da82c894a010">convolution_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;output_shape, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>)</div>
<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160;{</div>
<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; <span class="comment">// Create reference</span></div>
<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> dst{ output_shape, src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1, src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a35ccf2eb0c18a15feab2db98b307b78b">fixed_point_position</a>() };</div>
<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160;</div>
<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; <span class="comment">// Compute reference</span></div>
<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> width_in = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().x();</div>
<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> height_in = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().y();</div>
<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> depth_in = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().z();</div>
<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> width_out = dst.shape().x();</div>
<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> height_out = dst.shape().y();</div>
<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> depth_out = dst.shape().z();</div>
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> width_weights = weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().x();</div>
<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> height_weights = weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().y();</div>
<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> depth_weights = weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().z();</div>
<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> pad_xi = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aabcf39e3917f842dbc5fbb0d802f24d5">std::min</a>(static_cast&lt;int&gt;(info.<a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml#a9a9d6d62752247f733a3466b484e08b9">pad</a>().first), width_weights / 2);</div>
<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> pad_yi = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aabcf39e3917f842dbc5fbb0d802f24d5">std::min</a>(static_cast&lt;int&gt;(info.<a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml#a9a9d6d62752247f733a3466b484e08b9">pad</a>().second), height_weights / 2);</div>
<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> start_xi = width_weights / 2 - pad_xi;</div>
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> start_yi = height_weights / 2 - pad_yi;</div>
<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> end_xi = width_in - start_xi;</div>
<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> end_yi = height_in - start_yi;</div>
<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> stride_xi = info.<a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml#ad2f1ea50a9e215ad8ef612a724a4866a">stride</a>().first;</div>
<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> stride_yi = info.<a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml#ad2f1ea50a9e215ad8ef612a724a4866a">stride</a>().second;</div>
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> num_batches = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().total_size() / (width_in * height_in * depth_in);</div>
<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160;</div>
<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> r = 0; r &lt; num_batches; ++r)</div>
<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; {</div>
<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> yi = start_yi; yi &lt; end_yi; yi += stride_yi)</div>
<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; {</div>
<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> xi = start_xi; xi &lt; end_xi; xi += stride_xi)</div>
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; {</div>
<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> ofm = 0; ofm &lt; depth_out; ++ofm)</div>
<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; {</div>
<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; <span class="comment">// Compute input and output offsets</span></div>
<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> offset_in = r * width_in * height_in * depth_in;</div>
<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> xo = (xi - start_xi) / stride_xi;</div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> yo = (yi - start_yi) / stride_yi;</div>
<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> offset_out = xo + yo * width_out + ofm * width_out * height_out + r * width_out * height_out * depth_out;</div>
<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160;</div>
<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; <span class="comment">// Compute 3D convolution</span></div>
<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; convolution3d(src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">data</a>() + offset_in,</div>
<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">data</a>() + ofm * width_weights * height_weights * depth_weights,</div>
<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; bias.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">data</a>() + ofm,</div>
<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; dst.data() + offset_out,</div>
<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; xi, yi,</div>
<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; width_in, height_in, depth_in,</div>
<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; width_weights, height_weights,</div>
<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a35ccf2eb0c18a15feab2db98b307b78b">fixed_point_position</a>());</div>
<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; }</div>
<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; }</div>
<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; }</div>
<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; }</div>
<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160;</div>
<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; <span class="keywordflow">return</span> dst;</div>
<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160;}</div>
<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160;</div>
<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aeec300028ef21b06bc60da82c894a010">convolution_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> &amp;src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> &amp;weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> &amp;bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;output_shape,</div>
<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>);</div>
<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aeec300028ef21b06bc60da82c894a010">convolution_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> &amp;src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> &amp;weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> &amp;bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;output_shape,</div>
<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>);</div>
<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aeec300028ef21b06bc60da82c894a010">convolution_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint8_t&gt;</a> &amp;src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint8_t&gt;</a> &amp;weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint8_t&gt;</a> &amp;bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;output_shape,</div>
<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>);</div>
<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint16_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aeec300028ef21b06bc60da82c894a010">convolution_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint16_t&gt;</a> &amp;src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint16_t&gt;</a> &amp;weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint16_t&gt;</a> &amp;bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;output_shape,</div>
<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>);</div>
<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160;} <span class="comment">// namespace reference</span></div>
<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160;} <span class="comment">// namespace validation</span></div>
<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160;} <span class="comment">// namespace test</span></div>
<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160;} <span class="comment">// namespace arm_compute</span></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_aabcf39e3917f842dbc5fbb0d802f24d5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aabcf39e3917f842dbc5fbb0d802f24d5">arm_compute::test::fixed_point_arithmetic::detail::min</a></div><div class="ttdeci">fixed_point&lt; T &gt; min(fixed_point&lt; T &gt; x, fixed_point&lt; T &gt; y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00884">FixedPoint.h:884</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00038">TensorShape.h:38</a></div></div>
<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml_a9a9d6d62752247f733a3466b484e08b9"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml#a9a9d6d62752247f733a3466b484e08b9">arm_compute::PadStrideInfo::pad</a></div><div class="ttdeci">std::pair&lt; unsigned int, unsigned int &gt; pad() const </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00429">Types.h:429</a></div></div>
<div class="ttc" id="tests_2validation_2_c_p_p_2_convolution_layer_8h_xhtml"><div class="ttname"><a href="tests_2validation_2_c_p_p_2_convolution_layer_8h.xhtml">ConvolutionLayer.h</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a4103adbb45806b2f2002d44b91d0d206"><div class="ttname"><a href="_error_8h.xhtml#a4103adbb45806b2f2002d44b91d0d206">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(var)</div><div class="ttdoc">To avoid unused variables warnings. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00049">Error.h:49</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::test::SimpleTensor::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00265">SimpleTensor.h:265</a></div></div>
<div class="ttc" id="tests_2validation_2_helpers_8h_xhtml"><div class="ttname"><a href="tests_2validation_2_helpers_8h.xhtml">Helpers.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_aba5871b3e4a65d057ec1c28fce8b00ba"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">arm_compute::test::SimpleTensor::shape</a></div><div class="ttdeci">TensorShape shape() const override</div><div class="ttdoc">Shape of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00234">SimpleTensor.h:234</a></div></div>
<div class="ttc" id="tests_2validation_2_fixed_point_8h_xhtml"><div class="ttname"><a href="tests_2validation_2_fixed_point_8h.xhtml">FixedPoint.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a096668313a9a819d54a2e65ec21ff0cc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">arm_compute::test::validation::info</a></div><div class="ttdeci">src info() -&gt; set_format(Format::S16)</div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_aeec300028ef21b06bc60da82c894a010"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aeec300028ef21b06bc60da82c894a010">arm_compute::test::validation::reference::convolution_layer</a></div><div class="ttdeci">SimpleTensor&lt; T &gt; convolution_layer(const SimpleTensor&lt; T &gt; &amp;src, const SimpleTensor&lt; T &gt; &amp;weights, const SimpleTensor&lt; T &gt; &amp;bias, const TensorShape &amp;output_shape, const PadStrideInfo &amp;info)</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_p_p_2_convolution_layer_8cpp_source.xhtml#l00137">ConvolutionLayer.cpp:137</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a0c52a8f0085b55d907af7210ef2069d0"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">arm_compute::test::SimpleTensor::data</a></div><div class="ttdeci">const T * data() const </div><div class="ttdoc">Constant pointer to the underlying buffer. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00311">SimpleTensor.h:311</a></div></div>
<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml_ad2f1ea50a9e215ad8ef612a724a4866a"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml#ad2f1ea50a9e215ad8ef612a724a4866a">arm_compute::PadStrideInfo::stride</a></div><div class="ttdeci">std::pair&lt; unsigned int, unsigned int &gt; stride() const </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00425">Types.h:425</a></div></div>
<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00406">Types.h:406</a></div></div>
<div class="ttc" id="hwc_8hpp_xhtml_a0f61d63b009d0880a89c843bd50d8d76"><div class="ttname"><a href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a></div><div class="ttdeci">void * value</div><div class="ttdef"><b>Definition:</b> <a href="hwc_8hpp_source.xhtml#l00269">hwc.hpp:269</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">arm_compute::test::SimpleTensor</a></div><div class="ttdoc">Simple tensor object that stores elements in a consecutive chunk of memory. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00059">SimpleTensor.h:59</a></div></div>
<div class="ttc" id="namespacemali__userspace_xhtml_ad44b615021ed3ccb734fcaf583ef4a03"><div class="ttname"><a href="namespacemali__userspace.xhtml#ad44b615021ed3ccb734fcaf583ef4a03">mali_userspace::type</a></div><div class="ttdeci">uint32_t type</div><div class="ttdef"><b>Definition:</b> <a href="hwc_8hpp_source.xhtml#l00204">hwc.hpp:204</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_ad91bb73431b4de1f4946ed949d444849"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">arm_compute::test::fixed_point_arithmetic::detail::max</a></div><div class="ttdeci">fixed_point&lt; T &gt; max(fixed_point&lt; T &gt; x, fixed_point&lt; T &gt; y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00889">FixedPoint.h:889</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a35ccf2eb0c18a15feab2db98b307b78b"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a35ccf2eb0c18a15feab2db98b307b78b">arm_compute::test::SimpleTensor::fixed_point_position</a></div><div class="ttdeci">int fixed_point_position() const override</div><div class="ttdoc">The number of bits for the fractional part of the fixed point numbers. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00246">SimpleTensor.h:246</a></div></div>
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