arm_compute v18.05

[platform/upstream/armcl.git] / documentation / namespacearm__compute_1_1misc_1_1shape__calculator.xhtml

diff --git a/documentation/namespacearm__compute_1_1misc_1_1shape__calculator.xhtml b/documentation/namespacearm__compute_1_1misc_1_1shape__calculator.xhtml
index 7bae56f..87c2c65 100644 (file)
--- a/documentation/namespacearm__compute_1_1misc_1_1shape__calculator.xhtml
+++ b/documentation/namespacearm__compute_1_1misc_1_1shape__calculator.xhtml
@@ -40,7 +40,7 @@
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    <div id="projectname">Compute Library
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+   &#160;<span id="projectnumber">18.05</span>
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+<tr class="memitem:a1dcac9ac1b0bf5e37ccba49cf6a359df"><td class="memTemplParams" colspan="2">template&lt;typename T &gt; </td></tr>
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+<tr class="memitem:a333e4da262f8d985e8f9ab2c39a9c3f3"><td class="memTemplParams" colspan="2">template&lt;typename T &gt; </td></tr>
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 </table>
 <h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a333e4da262f8d985e8f9ab2c39a9c3f3"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::calculate_width_concatenate_shape </td>
+          <td>(</td>
+          <td class="paramtype">const std::vector&lt; T * &gt; &amp;&#160;</td>
+          <td class="paramname"><em>inputs_vector</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
+  </tr>
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+
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00382">382</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+
+<p>References <a class="el" href="_error_8h_source.xhtml#l00328">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00371">get_shape_from_info()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, <a class="el" href="_c_l_2_convolution_8cpp_source.xhtml#l00133">arm_compute::test::validation::shape</a>, and <a class="el" href="_dimensions_8h_source.xhtml#l00081">Dimensions&lt; T &gt;::x()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;{</div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;    TensorShape out_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1f0c8259e55c54d94621e259b2279f05">get_shape_from_info</a>(inputs_vector[0]);</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;    <span class="keywordtype">size_t</span> width = 0;</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;    <span class="keywordflow">for</span>(<span class="keyword">const</span> <span class="keyword">auto</span> &amp;tensor : inputs_vector)</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;    {</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;        <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(tensor == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;        <span class="keyword">const</span> TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1f0c8259e55c54d94621e259b2279f05">get_shape_from_info</a>(tensor);</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;        width += shape.x();</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    }</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;    out_shape.set(0, width);</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;</div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;    <span class="keywordflow">return</span> out_shape;</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00133">Convolution.cpp:133</a></div></div>
+<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a1f0c8259e55c54d94621e259b2279f05"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1f0c8259e55c54d94621e259b2279f05">arm_compute::misc::shape_calculator::get_shape_from_info</a></div><div class="ttdeci">TensorShape get_shape_from_info(ITensorInfo *info)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00376">ShapeCalculator.h:376</a></div></div>
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-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00115">115</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00110">110</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="_tensor_shape_8h_source.xhtml#l00077">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><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;    TensorShape col2im_shape{ input.tensor_shape() };</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    col2im_shape.set(0, convolved_dims.first);</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    col2im_shape.set(1, convolved_dims.second);</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    col2im_shape.set(2, input.tensor_shape()[0]);</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;    <span class="keywordflow">return</span> col2im_shape;</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;}</div></div><!-- fragment -->
+<p>References <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;{</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    TensorShape col2im_shape{ input.tensor_shape() };</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    col2im_shape.set(0, convolved_dims.first);</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;    col2im_shape.set(1, convolved_dims.second);</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    col2im_shape.set(2, input.tensor_shape()[0]);</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="keywordflow">return</span> col2im_shape;</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;}</div></div><!-- fragment -->
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-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00150">150</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00151">151</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00543">PadStrideInfo::pad()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;{</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;    TensorShape        scale_out_shape(input.tensor_shape());</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_x = input.dimension(0) + (input.dimension(0) - 1) * (sx - 1) + inner_border_right + 2 * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>.pad().first;</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_y = input.dimension(1) + (input.dimension(1) - 1) * (sy - 1) + inner_border_top + 2 * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>.pad().second;</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    scale_out_shape.set(0, out_x);</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    scale_out_shape.set(1, out_y);</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    <span class="keywordflow">return</span> scale_out_shape;</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;}</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>
+<p>References <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00637">PadStrideInfo::pad()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;{</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    TensorShape        scale_out_shape(input.tensor_shape());</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_x = input.dimension(0) + (input.dimension(0) - 1) * (sx - 1) + inner_border_right + 2 * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>.pad().first;</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_y = input.dimension(1) + (input.dimension(1) - 1) * (sy - 1) + inner_border_top + 2 * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>.pad().second;</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    scale_out_shape.set(0, out_x);</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    scale_out_shape.set(1, out_y);</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    <span class="keywordflow">return</span> scale_out_shape;</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;}</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>
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@@ -263,7 +320,7 @@ Functions</h2></td></tr>
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-          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape </td>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_deep_convolution_shape </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &amp;&#160;</td>
           <td class="paramname"><em>input</em>, </td>
@@ -293,11 +350,70 @@ Functions</h2></td></tr>
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-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00133">133</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00294">294</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, <a class="el" href="namespacearm__compute.xhtml#a3d3d8bf7b86db4d7d4ebfe5b332f41b3">arm_compute::scaled_dimensions()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;{</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;    <span class="keyword">const</span> TensorShape input_shape{ input.tensor_shape() };</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    <span class="keyword">const</span> TensorShape weights_shape{ weights.tensor_shape() };</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_width  = 0;</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_height = 0;</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    std::tie(output_width, output_height) = <a class="code" href="namespacearm__compute.xhtml#a3d3d8bf7b86db4d7d4ebfe5b332f41b3">scaled_dimensions</a>(input_shape.x(), input_shape.y(),</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;                                                              weights_shape.x(), weights_shape.y(),</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;                                                              conv_info);</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input_shape };</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, output_width);</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, output_height);</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
-<div class="ttc" id="namespacearm__compute_xhtml_a3d3d8bf7b86db4d7d4ebfe5b332f41b3"><div class="ttname"><a href="namespacearm__compute.xhtml#a3d3d8bf7b86db4d7d4ebfe5b332f41b3">arm_compute::scaled_dimensions</a></div><div class="ttdeci">const std::pair&lt; unsigned int, unsigned int &gt; scaled_dimensions(unsigned int width, unsigned int height, unsigned int kernel_width, unsigned int kernel_height, const PadStrideInfo &amp;pad_stride_info)</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode...</div></div>
+<p>References <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::CHANNEL</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">ITensorInfo::data_layout()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00340">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, <a class="el" href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">arm_compute::scaled_dimensions()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, and <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>.</p>
+<div class="fragment"><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;{</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    <span class="keyword">const</span> TensorShape input_shape{ input.tensor_shape() };</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    <span class="keyword">const</span> TensorShape weights_shape{ weights.tensor_shape() };</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_width   = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.data_layout(), DataLayoutDimension::WIDTH);</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_height  = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.data_layout(), DataLayoutDimension::HEIGHT);</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_channel = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.data_layout(), DataLayoutDimension::CHANNEL);</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_width         = input_shape[idx_width];</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_height        = input_shape[idx_height];</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weights_width       = weights_shape[idx_width];</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weights_height      = weights_shape[idx_height];</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weights_out_channel = weights_shape[3];</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>       output_width        = 0;</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>       output_height       = 0;</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    std::tie(output_width, output_height) = <a class="code" href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">scaled_dimensions</a>(input_width, input_height, weights_width, weights_height, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>);</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input_shape };</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_width, output_width);</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_height, output_height);</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_channel, weights_out_channel);</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acbf8f8a6dd185de04c1981c57a8963cf"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00360">Winograd.cpp:360</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ac78192301777700de24d8c75667baf35"><div class="ttname"><a href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">arm_compute::scaled_dimensions</a></div><div class="ttdeci">const std::pair&lt; unsigned int, unsigned int &gt; scaled_dimensions(unsigned int width, unsigned int height, unsigned int kernel_width, unsigned int kernel_height, const PadStrideInfo &amp;pad_stride_info, const Size2D &amp;dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode...</div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00340">Helpers.inl:340</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="ab51afcfdb9caea9e8185ae6a42ba4779"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape </td>
+          <td>(</td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>input</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>weights</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype"><a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>&#160;</td>
+          <td class="paramname"><em>conv_info</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">unsigned int&#160;</td>
+          <td class="paramname"><em>depth_multiplier</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
+  </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00128">128</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+
+<p>References <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::CHANNEL</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">ITensorInfo::data_layout()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00340">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, <a class="el" href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">arm_compute::scaled_dimensions()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, and <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>.</p>
+
+<p>Referenced by <a class="el" href="validation_2_n_e_o_n_2_depthwise_convolution_layer_8cpp_source.xhtml#l00057">arm_compute::test::validation::DATA_TEST_CASE()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;{</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    <span class="keyword">const</span> TensorShape input_shape{ input.tensor_shape() };</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    <span class="keyword">const</span> TensorShape weights_shape{ weights.tensor_shape() };</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> data_layout = input.data_layout();</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>        width_idx   = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(data_layout, DataLayoutDimension::WIDTH);</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>        height_idx  = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(data_layout, DataLayoutDimension::HEIGHT);</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>        channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(data_layout, DataLayoutDimension::CHANNEL);</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_width  = 0;</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_height = 0;</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    std::tie(output_width, output_height) = <a class="code" href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">scaled_dimensions</a>(input_shape[width_idx], input_shape[height_idx],</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;                                                              weights_shape[width_idx], weights_shape[height_idx],</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;                                                              <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>);</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input_shape };</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(width_idx, output_width);</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(height_idx, output_height);</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(channel_idx, input_shape[channel_idx] * depth_multiplier);</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acbf8f8a6dd185de04c1981c57a8963cf"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00360">Winograd.cpp:360</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ac78192301777700de24d8c75667baf35"><div class="ttname"><a href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">arm_compute::scaled_dimensions</a></div><div class="ttdeci">const std::pair&lt; unsigned int, unsigned int &gt; scaled_dimensions(unsigned int width, unsigned int height, unsigned int kernel_width, unsigned int kernel_height, const PadStrideInfo &amp;pad_stride_info, const Size2D &amp;dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode...</div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00340">Helpers.inl:340</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">Supported tensor data layouts. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00110">Types.h:110</a></div></div>
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@@ -345,16 +461,16 @@ Functions</h2></td></tr>
 </table>
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-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00178">178</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00211">211</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">ICloneable&lt; T &gt;::clone()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00168">compute_interleave_custom_shape()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00124">compute_transposed_shape()</a>, <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;{</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-&gt;tensor_shape() };</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    <span class="comment">// Transpose weights if the user hasn&#39;t done it</span></div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    <span class="keywordflow">if</span>(transpose_weights)</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    {</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(*input);</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;    <span class="comment">// If the we run multiple batches we need 1xW transpose, too.</span></div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;    <span class="keywordflow">if</span>(is_batched_fc_layer)</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    {</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(input-&gt;clone()-&gt;set_tensor_shape(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>));</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac18940223de1db7f6ed6c49119be7cd8">compute_interleave_custom_shape</a>(output_shape, interleave, interleave);</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    }</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ac18940223de1db7f6ed6c49119be7cd8"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac18940223de1db7f6ed6c49119be7cd8">arm_compute::misc::shape_calculator::compute_interleave_custom_shape</a></div><div class="ttdeci">TensorShape compute_interleave_custom_shape(const TensorShape &amp;input, const int x_interleave, const int y_interleave)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00168">ShapeCalculator.h:168</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a69cb11b5b37f94a6bea9eaad9d13cccf"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">arm_compute::misc::shape_calculator::compute_transposed_shape</a></div><div class="ttdeci">TensorShape compute_transposed_shape(const ITensorInfo &amp;input)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00124">ShapeCalculator.h:124</a></div></div>
+<p>References <a class="el" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">ICloneable&lt; T &gt;::clone()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00202">compute_interleave_custom_shape()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00119">compute_transposed_shape()</a>, <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;{</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-&gt;tensor_shape() };</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;    <span class="comment">// Transpose weights if the user hasn&#39;t done it</span></div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    <span class="keywordflow">if</span>(transpose_weights)</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    {</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(*input);</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;    }</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;    <span class="comment">// If we run multiple batches we need 1xW transpose, too.</span></div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;    <span class="keywordflow">if</span>(is_batched_fc_layer)</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    {</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(input-&gt;clone()-&gt;set_tensor_shape(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>));</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac18940223de1db7f6ed6c49119be7cd8">compute_interleave_custom_shape</a>(output_shape, interleave, interleave);</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    }</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ac18940223de1db7f6ed6c49119be7cd8"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac18940223de1db7f6ed6c49119be7cd8">arm_compute::misc::shape_calculator::compute_interleave_custom_shape</a></div><div class="ttdeci">TensorShape compute_interleave_custom_shape(const TensorShape &amp;input, const int x_interleave, const int y_interleave)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00202">ShapeCalculator.h:202</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a69cb11b5b37f94a6bea9eaad9d13cccf"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">arm_compute::misc::shape_calculator::compute_transposed_shape</a></div><div class="ttdeci">TensorShape compute_transposed_shape(const ITensorInfo &amp;input)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00119">ShapeCalculator.h:119</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
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@@ -362,11 +478,39 @@ Functions</h2></td></tr>
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-          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_im2col_shape </td>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_im2col_conv_shape </td>
           <td>(</td>
-          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &amp;&#160;</td>
-          <td class="paramname"><em>input</em></td><td>)</td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
+          <td class="paramname"><em>input</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &amp;&#160;</td>
+          <td class="paramname"><em>kernel_dims</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>conv_info</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bool&#160;</td>
+          <td class="paramname"><em>has_bias</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &amp;&#160;</td>
+          <td class="paramname"><em>dilation</em>&#160;</td>
+        </tr>
+        <tr>
           <td></td>
+          <td>)</td>
+          <td></td><td></td>
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-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00108">108</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00161">161</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="_tensor_shape_8h_source.xhtml#l00128">TensorShape::collapse()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;{</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    TensorShape shape_im2col{ input.tensor_shape() };</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    shape_im2col.collapse(3);</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    <span class="keywordflow">return</span> shape_im2col;</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;}</div></div><!-- fragment -->
+<p>References <a class="el" href="_size2_d_8h_source.xhtml#l00053">Size2D::area()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::CHANNEL</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">ITensorInfo::data_layout()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00340">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="_size2_d_8h_source.xhtml#l00093">Size2D::height</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, <a class="el" href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">arm_compute::scaled_dimensions()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_size2_d_8h_source.xhtml#l00092">Size2D::width</a>, and <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>.</p>
+<div class="fragment"><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;{</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    <span class="comment">// The output shape will be the 2D shape used as input for GEMM [ out_channels * kernel_area, num_elems_per_out_channel ]</span></div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-&gt;tensor_shape() };</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> data_layout = input-&gt;data_layout();</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>        width_idx   = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(data_layout, DataLayoutDimension::WIDTH);</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>        height_idx  = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(data_layout, DataLayoutDimension::HEIGHT);</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>        channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(data_layout, DataLayoutDimension::CHANNEL);</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    std::pair&lt;unsigned int, unsigned int&gt; out_dims = <a class="code" href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">scaled_dimensions</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[width_idx], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[height_idx], kernel_dims.width, kernel_dims.height, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>, dilation);</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[channel_idx] * kernel_dims.area() + (has_bias ? 1 : 0)));</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, (out_dims.first * out_dims.second));</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(2, 1);</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acbf8f8a6dd185de04c1981c57a8963cf"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00360">Winograd.cpp:360</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ac78192301777700de24d8c75667baf35"><div class="ttname"><a href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">arm_compute::scaled_dimensions</a></div><div class="ttdeci">const std::pair&lt; unsigned int, unsigned int &gt; scaled_dimensions(unsigned int width, unsigned int height, unsigned int kernel_width, unsigned int kernel_height, const PadStrideInfo &amp;pad_stride_info, const Size2D &amp;dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode...</div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00340">Helpers.inl:340</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">Supported tensor data layouts. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00110">Types.h:110</a></div></div>
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@@ -390,7 +539,7 @@ Functions</h2></td></tr>
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-          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_im2col_shape </td>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_im2col_fc_shape </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>input</em>, </td>
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-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00160">160</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00179">179</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
 <p>References <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><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;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-&gt;tensor_shape() };</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;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.collapse(num_input_dimensions);</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">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
+<div class="fragment"><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;{</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-&gt;tensor_shape() };</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.collapse(num_input_dimensions);</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
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+<div class="memitem">
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+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_im2col_flatten_shape </td>
+          <td>(</td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
+          <td class="paramname"><em>input</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
+  </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00187">187</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+
+<p>References <a class="el" href="_error_8h_source.xhtml#l00328">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">ITensorInfo::num_dimensions()</a>, <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><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;    <span class="comment">// The output shape will be the flatten version of the input (i.e. [ width * height * channels, 1, 1, ... ] ). Used for FlattenLayer.</span></div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input-&gt;num_dimensions() &lt; 3);</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;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-&gt;tensor_shape() };</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> flatten_shape = input-&gt;dimension(0) * input-&gt;dimension(1) * input-&gt;dimension(2);</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, flatten_shape);</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.remove_dimension(1);</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.remove_dimension(1);</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;}</div><div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
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@@ -459,12 +638,12 @@ Functions</h2></td></tr>
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-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00168">168</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00202">202</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
 <p>References <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>.</p>
 
-<p>Referenced by <a class="el" href="_shape_calculator_8h_source.xhtml#l00178">compute_fully_connected_reshaped_weights_shape()</a>.</p>
-<div class="fragment"><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;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input };</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.x() * x_interleave);</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, std::ceil(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.y() / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(y_interleave)));</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
+<p>Referenced by <a class="el" href="_shape_calculator_8h_source.xhtml#l00211">compute_fully_connected_reshaped_weights_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;{</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input };</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.x() * x_interleave);</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, std::ceil(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.y() / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(y_interleave)));</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
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-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00054">54</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00056">56</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+
+<p>References <a class="el" href="_error_8h_source.xhtml#l00328">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;{</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <span class="comment">// The interleaved output matrix will have the following shape: [ a_height * W, ceil(a_width / W) ] where W = 4 * mult_interleave4x4_height</span></div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(mult_interleave4x4_height &lt; 1);</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>   interleave_width = 4 * mult_interleave4x4_height;</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;    TensorShape shape_interleaved_a{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>.tensor_shape() };</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    shape_interleaved_a.set(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>.dimension(0) * interleave_width);</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;    shape_interleaved_a.set(1, std::ceil(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>.dimension(1) / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(interleave_width)));</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">return</span> shape_interleaved_a;</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aac40b7097f2bda9274ae07fa33d15a79"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">arm_compute::test::validation::a</a></div><div class="ttdeci">CLTensor a</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00121">GEMM.cpp:121</a></div></div>
+<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="a1b843e3850ed7324d11f77882cc597ae"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_min_max_shape </td>
+          <td>(</td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
+          <td class="paramname"><em>input</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
+  </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00319">319</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="core_2_error_8h_source.xhtml#l00306">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00077">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><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">// The interleaved output matrix will have the following shape: [ a_height * W, ceil(a_width / W) ] where W = 4 * mult_interleave4x4_height</span></div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    <a class="code" href="core_2_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(mult_interleave4x4_height &lt; 1);</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>   interleave_width = 4 * mult_interleave4x4_height;</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    TensorShape shape_interleaved_a{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>.tensor_shape() };</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    shape_interleaved_a.set(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>.dimension(0) * interleave_width);</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;    shape_interleaved_a.set(1, std::ceil(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>.dimension(1) / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(interleave_width)));</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;    <span class="keywordflow">return</span> shape_interleaved_a;</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aac40b7097f2bda9274ae07fa33d15a79"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">arm_compute::test::validation::a</a></div><div class="ttdeci">CLTensor a</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00121">GEMM.cpp:121</a></div></div>
-<div class="ttc" id="core_2_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="core_2_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="core_2_error_8h_source.xhtml#l00306">Error.h:306</a></div></div>
+<p>References <a class="el" href="_window_8h_source.xhtml#l00043">Window::DimX</a>, <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;{</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-&gt;tensor_shape() };</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(Window::DimX, 2);</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.remove_dimension(1);</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.remove_dimension(1);</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
 </div><!-- fragment -->
 </div>
 </div>
+<a class="anchor" id="adca241b012a5e00ddfcdc5a8db05a2a3"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_mm_shape </td>
+          <td>(</td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>input0</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>input1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bool&#160;</td>
+          <td class="paramname"><em>is_interleaved_transposed</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml">GEMMReshapeInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>reshape_info</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
+  </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00361">361</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+
+<p>References <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01028">GEMMReshapeInfo::m()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01036">GEMMReshapeInfo::n()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;{</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;    TensorShape tensor_shape{ input0.tensor_shape() };</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;    tensor_shape.set(0, is_interleaved_transposed ? reshape_info.n() : input1.dimension(0));</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;    tensor_shape.set(1, is_interleaved_transposed ? reshape_info.m() : input0.dimension(1));</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;    <span class="keywordflow">return</span> tensor_shape;</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;}</div></div><!-- fragment -->
+</div>
+</div>
 <a class="anchor" id="a593fb7ecc281425b190cd6f20164b1a3"></a>
 <div class="memitem">
 <div class="memproto">
@@ -540,14 +798,57 @@ Functions</h2></td></tr>
 </table>
 </div><div class="memdoc">
 
-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00037">37</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00039">39</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, <a class="el" href="arm__compute_2core_2_helpers_8h_source.xhtml#l00509">arm_compute::permute()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><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;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = input.tensor_shape();</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;    <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">permute</a>(output_shape, perm);</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;}</div><div class="ttc" id="namespacearm__compute_xhtml_a21c3e11887f3acf9284ca763372c7da0"><div class="ttname"><a href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">arm_compute::permute</a></div><div class="ttdeci">void permute(Dimensions&lt; T &gt; &amp;dimensions, const PermutationVector &amp;perm)</div><div class="ttdoc">Permutes given Dimensions according to a permutation vector. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00509">Helpers.h:509</a></div></div>
+<p>References <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, <a class="el" href="arm__compute_2core_2_helpers_8h_source.xhtml#l00536">arm_compute::permute()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><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;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = input.tensor_shape();</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;    <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">permute</a>(output_shape, perm);</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;}</div><div class="ttc" id="namespacearm__compute_xhtml_a21c3e11887f3acf9284ca763372c7da0"><div class="ttname"><a href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">arm_compute::permute</a></div><div class="ttdeci">void permute(Dimensions&lt; T &gt; &amp;dimensions, const PermutationVector &amp;perm)</div><div class="ttdoc">Permutes given Dimensions according to a permutation vector. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00536">Helpers.h:536</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
 </div><!-- fragment -->
 </div>
 </div>
+<a class="anchor" id="ad242bedd6845b8fc13ade41cfc062c83"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_pool_shape </td>
+          <td>(</td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>input</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype"><a class="el" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>&#160;</td>
+          <td class="paramname"><em>pool_info</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
+  </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00329">329</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+
+<p>References <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">ITensorInfo::data_layout()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00340">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="_size2_d_8h_source.xhtml#l00093">Size2D::height</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00759">PoolingLayerInfo::is_global_pooling()</a>, <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00749">PoolingLayerInfo::pad_stride_info()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00744">PoolingLayerInfo::pool_size()</a>, <a class="el" href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">arm_compute::scaled_dimensions()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_size2_d_8h_source.xhtml#l00092">Size2D::width</a>, and <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>.</p>
+
+<p>Referenced by <a class="el" href="validation_2reference_2_pooling_layer_8cpp_source.xhtml#l00042">arm_compute::test::validation::reference::pooling_layer()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;{</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pooled_w = 0;</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pooled_h = 0;</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input.tensor_shape() };</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span>         is_global_pooling = pool_info.is_global_pooling();</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx_width         = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.data_layout(), DataLayoutDimension::WIDTH);</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx_height        = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.data_layout(), DataLayoutDimension::HEIGHT);</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pool_size_x       = is_global_pooling ? <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_width] : pool_info.pool_size().width;</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pool_size_y       = is_global_pooling ? <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_height] : pool_info.pool_size().height;</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    std::tie(pooled_w, pooled_h) = <a class="code" href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">scaled_dimensions</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_width],</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;                                                     <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_height],</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;                                                     pool_size_x,</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;                                                     pool_size_y,</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;                                                     pool_info.pad_stride_info());</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_width, pooled_w);</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_height, pooled_h);</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ac78192301777700de24d8c75667baf35"><div class="ttname"><a href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">arm_compute::scaled_dimensions</a></div><div class="ttdeci">const std::pair&lt; unsigned int, unsigned int &gt; scaled_dimensions(unsigned int width, unsigned int height, unsigned int kernel_width, unsigned int kernel_height, const PadStrideInfo &amp;pad_stride_info, const Size2D &amp;dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode...</div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00340">Helpers.inl:340</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
 <a class="anchor" id="a60ce6c017f70d978b48b101ce314969e"></a>
 <div class="memitem">
 <div class="memproto">
@@ -570,10 +871,10 @@ Functions</h2></td></tr>
 </table>
 </div><div class="memdoc">
 
-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00087">87</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00089">89</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="_tensor_shape_8h_source.xhtml#l00106">TensorShape::remove_dimension()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;{</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    TensorShape shape_vector_sum_col{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.tensor_shape() };</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    <span class="keywordflow">if</span>(shape_vector_sum_col.num_dimensions() &gt; 1)</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;    {</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        shape_vector_sum_col.remove_dimension(1);</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;</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    <span class="keywordflow">return</span> shape_vector_sum_col;</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7b8004eef325a40dd43eb80755610fff"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">arm_compute::test::validation::b</a></div><div class="ttdeci">CLTensor b</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00122">GEMM.cpp:122</a></div></div>
+<p>References <a class="el" href="_tensor_shape_8h_source.xhtml#l00110">TensorShape::remove_dimension()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><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;    TensorShape shape_vector_sum_col{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.tensor_shape() };</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    <span class="keywordflow">if</span>(shape_vector_sum_col.num_dimensions() &gt; 1)</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;        shape_vector_sum_col.remove_dimension(1);</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    }</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    <span class="keywordflow">return</span> shape_vector_sum_col;</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7b8004eef325a40dd43eb80755610fff"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">arm_compute::test::validation::b</a></div><div class="ttdeci">CLTensor b</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00122">GEMM.cpp:122</a></div></div>
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 </div>
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@@ -599,10 +900,49 @@ Functions</h2></td></tr>
 </table>
 </div><div class="memdoc">
 
-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00097">97</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00099">99</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_window_8h_source.xhtml#l00043">Window::DimX</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">ITensorInfo::num_dimensions()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00106">TensorShape::remove_dimension()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00077">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;{</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    TensorShape shape_vector_sum_row{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>.tensor_shape() };</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    shape_vector_sum_row.set(Window::DimX, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>.dimension(1));</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>.num_dimensions() &gt; 1)</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;        shape_vector_sum_row.remove_dimension(1);</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    }</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    <span class="keywordflow">return</span> shape_vector_sum_row;</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aac40b7097f2bda9274ae07fa33d15a79"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">arm_compute::test::validation::a</a></div><div class="ttdeci">CLTensor a</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00121">GEMM.cpp:121</a></div></div>
+<p>References <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_window_8h_source.xhtml#l00043">Window::DimX</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">ITensorInfo::num_dimensions()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00110">TensorShape::remove_dimension()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;{</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    TensorShape shape_vector_sum_row{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>.tensor_shape() };</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    shape_vector_sum_row.set(Window::DimX, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>.dimension(1));</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>.num_dimensions() &gt; 1)</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    {</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;        shape_vector_sum_row.remove_dimension(1);</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    }</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    <span class="keywordflow">return</span> shape_vector_sum_row;</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aac40b7097f2bda9274ae07fa33d15a79"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">arm_compute::test::validation::a</a></div><div class="ttdeci">CLTensor a</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00121">GEMM.cpp:121</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="af98bc3ef5c65dbb63bc79700ccdd043b"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_rnn_shape </td>
+          <td>(</td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
+          <td class="paramname"><em>input</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">const unsigned int&#160;</td>
+          <td class="paramname"><em>batch_size</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
+  </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00354">354</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+
+<p>References <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;{</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-&gt;tensor_shape() };</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, batch_size);</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
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@@ -628,10 +968,10 @@ Functions</h2></td></tr>
 </table>
 </div><div class="memdoc">
 
-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00065">65</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00067">67</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00077">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><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">// The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ]</span></div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    TensorShape shape_transposed1xW_b{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.tensor_shape() };</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    shape_transposed1xW_b.set(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.dimension(1) * 16);</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;    shape_transposed1xW_b.set(1, std::ceil(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.dimension(0) / 16.f));</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    <span class="keywordflow">return</span> shape_transposed1xW_b;</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7b8004eef325a40dd43eb80755610fff"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">arm_compute::test::validation::b</a></div><div class="ttdeci">CLTensor b</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00122">GEMM.cpp:122</a></div></div>
+<p>References <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;{</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    <span class="comment">// The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ]</span></div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;    TensorShape shape_transposed1xW_b{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.tensor_shape() };</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    shape_transposed1xW_b.set(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.dimension(1) * 16);</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    shape_transposed1xW_b.set(1, std::ceil(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.dimension(0) / 16.f));</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    <span class="keywordflow">return</span> shape_transposed1xW_b;</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7b8004eef325a40dd43eb80755610fff"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">arm_compute::test::validation::b</a></div><div class="ttdeci">CLTensor b</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00122">GEMM.cpp:122</a></div></div>
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@@ -667,10 +1007,10 @@ Functions</h2></td></tr>
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-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00074">74</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00076">76</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="core_2_error_8h_source.xhtml#l00306">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">ITensorInfo::element_size()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00077">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><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;    <span class="comment">// Note: mult_transpose1xW_width expresses the number of chunks with size 1x(W) we want to store on the same row</span></div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    <span class="comment">//       The transpose1xW output matrix will have the following shape:</span></div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    <span class="comment">//       [ b_height * W, ceil(b_width / W) ] where W = (16 / element size of the tensor) * mult_transpose1xW_width</span></div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    <a class="code" href="core_2_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(mult_transpose1xW_width &lt; 1);</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    TensorShape  shape_transposed1xW_b{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.tensor_shape() };</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> transpose_width = (16 / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.element_size()) * mult_transpose1xW_width;</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    shape_transposed1xW_b.set(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.dimension(1) * transpose_width);</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    shape_transposed1xW_b.set(1, static_cast&lt;size_t&gt;(std::ceil(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.dimension(0) / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(transpose_width))));</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;    <span class="keywordflow">return</span> shape_transposed1xW_b;</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;}</div><div class="ttc" id="core_2_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="core_2_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="core_2_error_8h_source.xhtml#l00306">Error.h:306</a></div></div>
+<p>References <a class="el" href="_error_8h_source.xhtml#l00328">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">ITensorInfo::element_size()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><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;    <span class="comment">// Note: mult_transpose1xW_width expresses the number of chunks with size 1x(W) we want to store on the same row</span></div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    <span class="comment">//       The transpose1xW output matrix will have the following shape:</span></div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    <span class="comment">//       [ b_height * W, ceil(b_width / W) ] where W = (16 / element size of the tensor) * mult_transpose1xW_width</span></div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(mult_transpose1xW_width &lt; 1);</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    TensorShape  shape_transposed1xW_b{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.tensor_shape() };</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> transpose_width = (16 / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.element_size()) * mult_transpose1xW_width;</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    shape_transposed1xW_b.set(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.dimension(1) * transpose_width);</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;    shape_transposed1xW_b.set(1, static_cast&lt;size_t&gt;(std::ceil(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>.dimension(0) / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(transpose_width))));</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    <span class="keywordflow">return</span> shape_transposed1xW_b;</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;}</div><div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7b8004eef325a40dd43eb80755610fff"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">arm_compute::test::validation::b</a></div><div class="ttdeci">CLTensor b</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00122">GEMM.cpp:122</a></div></div>
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@@ -697,12 +1037,12 @@ Functions</h2></td></tr>
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-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00124">124</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00119">119</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00077">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<p>References <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
 
-<p>Referenced by <a class="el" href="_shape_calculator_8h_source.xhtml#l00178">compute_fully_connected_reshaped_weights_shape()</a>.</p>
-<div class="fragment"><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;    TensorShape shape_transposed{ input.tensor_shape() };</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    shape_transposed.set(0, input.dimension(1));</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    shape_transposed.set(1, input.dimension(0));</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    <span class="keywordflow">return</span> shape_transposed;</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;}</div></div><!-- fragment -->
+<p>Referenced by <a class="el" href="_shape_calculator_8h_source.xhtml#l00211">compute_fully_connected_reshaped_weights_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;{</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    TensorShape shape_transposed{ input.tensor_shape() };</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;    shape_transposed.set(0, input.dimension(1));</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    shape_transposed.set(1, input.dimension(0));</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="keywordflow">return</span> shape_transposed;</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;}</div></div><!-- fragment -->
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@@ -737,10 +1077,199 @@ Functions</h2></td></tr>
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-<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00043">43</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00045">45</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
 
-<p>References <a class="el" href="_tensor_shape_8h_source.xhtml#l00128">TensorShape::collapse()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;{</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;    <span class="comment">// Calculate output shape</span></div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;    TensorShape weights_reshaped{ weights.tensor_shape() };</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;    weights_reshaped.collapse(3);</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> tmp_dim = weights_reshaped[0];</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;    weights_reshaped.set(0, weights_reshaped[1]);</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;    weights_reshaped.set(1, tmp_dim + (has_bias ? 1 : 0));</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;    <span class="keywordflow">return</span> weights_reshaped;</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;}</div></div><!-- fragment -->
+<p>References <a class="el" href="_tensor_shape_8h_source.xhtml#l00132">TensorShape::collapse()</a>, and <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;{</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;    <span class="comment">// Calculate output shape</span></div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    TensorShape weights_reshaped{ weights.tensor_shape() };</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;    weights_reshaped.collapse(3);</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> tmp_dim = weights_reshaped[0];</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;    weights_reshaped.set(0, weights_reshaped[1]);</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;    weights_reshaped.set(1, tmp_dim + (has_bias ? 1 : 0));</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;    <span class="keywordflow">return</span> weights_reshaped;</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;}</div></div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="a25e3751f07d4b2771a05d8d01a7f7620"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape </td>
+          <td>(</td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>input</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">const <a class="el" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>winograd_info</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
+  </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00231">231</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+
+<p>References <a class="el" href="_size2_d_8h_source.xhtml#l00053">Size2D::area()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::CHANNEL</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">ITensorInfo::data_layout()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_window_8h_source.xhtml#l00043">Window::DimX</a>, <a class="el" href="_window_8h_source.xhtml#l00045">Window::DimY</a>, <a class="el" href="_window_8h_source.xhtml#l00047">Window::DimZ</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00340">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="_size2_d_8h_source.xhtml#l00093">Size2D::height</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01156">WinogradInfo::kernel_size</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01155">WinogradInfo::output_tile_size</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_size2_d_8h_source.xhtml#l00092">Size2D::width</a>, and <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>.</p>
+
+<p>Referenced by <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00183">arm_compute::test::validation::DATA_TEST_CASE()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;{</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;    TensorShape tensor_shape{ input.tensor_shape() };</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;    <span class="keyword">const</span> Size2D kernel_size      = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.kernel_size;</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;    <span class="keyword">const</span> Size2D output_tile_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.output_tile_size;</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    <span class="keyword">const</span> Size2D input_tile_size  = Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1);</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;    tensor_shape.remove_dimension(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.data_layout(), DataLayoutDimension::WIDTH));</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;    tensor_shape.set(Window::DimX, input.dimension(3));</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;    tensor_shape.set(Window::DimY, input.dimension(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.data_layout(), DataLayoutDimension::CHANNEL)));</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    tensor_shape.set(Window::DimZ, input_tile_size.area());</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    <span class="keywordflow">return</span> tensor_shape;</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a809d18ccde99d938a68cb90ef53aa749"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">arm_compute::test::validation::winograd_info</a></div><div class="ttdeci">winograd_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00173">Winograd.cpp:173</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00340">Helpers.inl:340</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="a04249f91ec2964d21a91bb7038821000"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_winograd_input_transform_shape </td>
+          <td>(</td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>input</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">const <a class="el" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>winograd_info</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
+  </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00246">246</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+
+<p>References <a class="el" href="_size2_d_8h_source.xhtml#l00053">Size2D::area()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::CHANNEL</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00360">arm_compute::test::validation::conv_info</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01158">WinogradInfo::convolution_info</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">ITensorInfo::data_layout()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00340">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="_size2_d_8h_source.xhtml#l00093">Size2D::height</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01156">WinogradInfo::kernel_size</a>, <a class="el" href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">arm_compute::test::validation::output_shape</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01155">WinogradInfo::output_tile_size</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00660">PadStrideInfo::pad_bottom()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00645">PadStrideInfo::pad_left()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00650">PadStrideInfo::pad_right()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00655">PadStrideInfo::pad_top()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_size2_d_8h_source.xhtml#l00092">Size2D::width</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00081">Dimensions&lt; T &gt;::x()</a>, and <a class="el" href="_dimensions_8h_source.xhtml#l00086">Dimensions&lt; T &gt;::y()</a>.</p>
+
+<p>Referenced by <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00105">arm_compute::test::validation::DATA_TEST_CASE()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;{</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;    <span class="keyword">const</span> PadStrideInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>        = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.convolution_info;</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;    <span class="keyword">const</span> Size2D        kernel_size      = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.kernel_size;</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    <span class="keyword">const</span> Size2D        output_tile_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.output_tile_size;</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;    <span class="keyword">const</span> Size2D        input_tile_size  = Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1);</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;    <span class="comment">// Compute height</span></div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_tiles_x = std::ceil((input.tensor_shape().x() - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast&lt;float&gt;(output_tile_size.width));</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_tiles_y = std::ceil((input.tensor_shape().y() - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast&lt;float&gt;(output_tile_size.height));</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width  = input.tensor_shape()[<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.data_layout(), DataLayoutDimension::CHANNEL)];</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = num_tiles_x * num_tiles_y;</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth  = input_tile_size.area();</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;</div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;    TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input.tensor_shape() };</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, width);</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, height);</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(2, depth);</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a809d18ccde99d938a68cb90ef53aa749"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">arm_compute::test::validation::winograd_info</a></div><div class="ttdeci">winograd_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00173">Winograd.cpp:173</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00115">GEMM.cpp:115</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acbf8f8a6dd185de04c1981c57a8963cf"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00360">Winograd.cpp:360</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00340">Helpers.inl:340</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="a5699c316d27b41f0790827791e88ae26"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape </td>
+          <td>(</td>
+          <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>input</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">const <a class="el" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &amp;&#160;</td>
+          <td class="paramname"><em>winograd_info</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
+  </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00268">268</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+
+<p>References <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::CHANNEL</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00360">arm_compute::test::validation::conv_info</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01158">WinogradInfo::convolution_info</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00340">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="_size2_d_8h_source.xhtml#l00093">Size2D::height</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01157">WinogradInfo::input_dimensions</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01156">WinogradInfo::kernel_size</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01159">WinogradInfo::output_data_layout</a>, <a class="el" href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">arm_compute::scaled_dimensions()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_size2_d_8h_source.xhtml#l00092">Size2D::width</a>, and <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>.</p>
+
+<p>Referenced by <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00288">arm_compute::test::validation::DATA_TEST_CASE()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;{</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    <span class="keyword">const</span> PadStrideInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>        = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.convolution_info;</div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;    <span class="keyword">const</span> Size2D        kernel_size      = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.kernel_size;</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;    <span class="keyword">const</span> Size2D        input_dimensions = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.input_dimensions;</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a>    data_layout      = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.output_data_layout;</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    <span class="comment">// Compute output shape</span></div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_width  = 0;</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_height = 0;</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    std::tie(output_width, output_height) = <a class="code" href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">scaled_dimensions</a>(input_dimensions.width, input_dimensions.height,</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;                                                              kernel_size.width, kernel_size.height, conv_info);</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    TensorShape tensor_shape{ input.tensor_shape() };</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;    <span class="comment">// Output dimension</span></div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_w = output_width;</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_h = output_height;</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_c = input.dimension(0);</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    tensor_shape.set(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(data_layout, DataLayoutDimension::WIDTH), out_w);</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    tensor_shape.set(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(data_layout, DataLayoutDimension::HEIGHT), out_h);</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;    tensor_shape.set(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(data_layout, DataLayoutDimension::CHANNEL), out_c);</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    <span class="keywordflow">return</span> tensor_shape;</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a809d18ccde99d938a68cb90ef53aa749"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">arm_compute::test::validation::winograd_info</a></div><div class="ttdeci">winograd_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00173">Winograd.cpp:173</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acbf8f8a6dd185de04c1981c57a8963cf"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00360">Winograd.cpp:360</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ac78192301777700de24d8c75667baf35"><div class="ttname"><a href="namespacearm__compute.xhtml#ac78192301777700de24d8c75667baf35">arm_compute::scaled_dimensions</a></div><div class="ttdeci">const std::pair&lt; unsigned int, unsigned int &gt; scaled_dimensions(unsigned int width, unsigned int height, unsigned int kernel_width, unsigned int kernel_height, const PadStrideInfo &amp;pad_stride_info, const Size2D &amp;dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode...</div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00340">Helpers.inl:340</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">Supported tensor data layouts. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00110">Types.h:110</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="a1dcac9ac1b0bf5e37ccba49cf6a359df"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::get_shape_from_info </td>
+          <td>(</td>
+          <td class="paramtype">T *&#160;</td>
+          <td class="paramname"><em>info</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
+  </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00371">371</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+
+<p>Referenced by <a class="el" href="_shape_calculator_8h_source.xhtml#l00382">calculate_width_concatenate_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;{</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>-&gt;info()-&gt;tensor_shape();</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;}</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><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="a1f0c8259e55c54d94621e259b2279f05"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+  <tr>
+  <td class="mlabels-left">
+      <table class="memname">
+        <tr>
+          <td class="memname"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> arm_compute::misc::shape_calculator::get_shape_from_info </td>
+          <td>(</td>
+          <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
+          <td class="paramname"><em>info</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+  </td>
+  <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
+  </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_shape_calculator_8h_source.xhtml#l00376">376</a> of file <a class="el" href="_shape_calculator_8h_source.xhtml">ShapeCalculator.h</a>.</p>
+
+<p>References <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;{</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>-&gt;tensor_shape();</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;}</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><!-- fragment -->
 </div>
 </div>
 </div><!-- contents -->
@@ -749,7 +1278,7 @@ Functions</h2></td></tr>
 <div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
   <ul>
     <li class="navelem"><a class="el" href="namespacearm__compute.xhtml">arm_compute</a></li><li class="navelem"><a class="el" href="namespacearm__compute_1_1misc.xhtml">misc</a></li><li class="navelem"><a class="el" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml">shape_calculator</a></li>
-    <li class="footer">Generated on Fri Mar 2 2018 12:37:59 for Compute Library by
+    <li class="footer">Generated on Wed May 23 2018 11:36:43 for Compute Library by
     <a href="http://www.doxygen.org/index.html">
     <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.11 </li>
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