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<div class="title">ShapeCalculator.h</div> </div>
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-<a href="_shape_calculator_8h.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017, 2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#ifndef __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#define __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_i_tensor_info_8h.xhtml">arm_compute/core/ITensorInfo.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_utils_8h.xhtml">arm_compute/core/Utils.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> {</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">namespace </span>misc</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> {</div><div class="line"><a name="l00034"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml"> 34</a></span> <span class="keyword">namespace </span>shape_calculator</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> {</div><div class="line"><a name="l00036"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a593fb7ecc281425b190cd6f20164b1a3"> 36</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a593fb7ecc281425b190cd6f20164b1a3">compute_permutation_output_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_strides.xhtml">PermutationVector</a> &perm)</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">permute</a>(output_shape, perm);</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <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="l00041"></a><span class="lineno"> 41</span> }</div><div class="line"><a name="l00042"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a4bf459b95015d89d6fccb22c15f3b4f5"> 42</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a4bf459b95015d89d6fccb22c15f3b4f5">compute_interleaved_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>)</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> {</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="comment">// The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ]</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_interleaved_a{ a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  shape_interleaved_a.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a0cb0e1f5da2e1cc2e0ea5690450f53e8">set</a>(0, a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) * 4);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  shape_interleaved_a.set(1, std::ceil(a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) / 4.f));</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">return</span> shape_interleaved_a;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> }</div><div class="line"><a name="l00051"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a70a2ef9fd754b5798a0a92656f8b5fcf"> 51</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a70a2ef9fd754b5798a0a92656f8b5fcf">compute_transpose1xW_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>)</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <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="l00054"></a><span class="lineno"> 54</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_transposed1xW_b{ b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  shape_transposed1xW_b.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a0cb0e1f5da2e1cc2e0ea5690450f53e8">set</a>(0, b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) * 16);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  shape_transposed1xW_b.set(1, std::ceil(b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) / 16.f));</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">return</span> shape_transposed1xW_b;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> }</div><div class="line"><a name="l00060"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a68eacb752d795a850ba6bfe7b226f29c"> 60</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a68eacb752d795a850ba6bfe7b226f29c">compute_transpose1xW_with_element_size_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> {</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="comment">// The transpose1xW output matrix will have the following shape:</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="comment">// [ b_height * (16 / element_size), ceil(b_width / (16.0f / element_size) ]</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_transposed1xW_b{ b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> transpose_width = 16 / b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">element_size</a>();</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  shape_transposed1xW_b.set(0, b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) * transpose_width);</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  shape_transposed1xW_b.set(1, static_cast<size_t>(std::ceil(b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) / <span class="keyword">static_cast<</span><span class="keywordtype">float</span><span class="keyword">></span>(transpose_width))));</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordflow">return</span> shape_transposed1xW_b;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> }</div><div class="line"><a name="l00071"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a60ce6c017f70d978b48b101ce314969e"> 71</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a60ce6c017f70d978b48b101ce314969e">compute_reductionA_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>)</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_vector_sum_col{ b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordflow">if</span>(shape_vector_sum_col.num_dimensions() > 1)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  {</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  shape_vector_sum_col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">remove_dimension</a>(1);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  }</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordflow">return</span> shape_vector_sum_col;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> }</div><div class="line"><a name="l00081"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69f9b3191aafc4905f9d029ff9d48fea"> 81</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69f9b3191aafc4905f9d029ff9d48fea">compute_reductionB_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> {</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_vector_sum_row{ a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  shape_vector_sum_row.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a0cb0e1f5da2e1cc2e0ea5690450f53e8">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1));</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordflow">if</span>(a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1)</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  {</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  shape_vector_sum_row.remove_dimension(1);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  }</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> </div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordflow">return</span> shape_vector_sum_row;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> }</div><div class="line"><a name="l00092"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a198e3937aafccfdf3cb6327a87fc1aec"> 92</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a198e3937aafccfdf3cb6327a87fc1aec">compute_im2col_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input)</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_im2col{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">collapse</a>(3);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">return</span> shape_im2col;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> }</div><div class="line"><a name="l00099"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf"> 99</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input)</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_transposed{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  shape_transposed.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a0cb0e1f5da2e1cc2e0ea5690450f53e8">set</a>(0, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1));</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  shape_transposed.set(1, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0));</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keywordflow">return</span> shape_transposed;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> }</div><div class="line"><a name="l00108"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8cf251400728a619fd1026788988da34"> 108</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8cf251400728a619fd1026788988da34">compute_depthwise_convolution_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &weights, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_info)</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> {</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> input_shape{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> weights_shape{ weights.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_width = 0;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_height = 0;</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  std::tie(output_width, output_height) = <a class="code" href="namespacearm__compute.xhtml#a3d3d8bf7b86db4d7d4ebfe5b332f41b3">scaled_dimensions</a>(input_shape.x(), input_shape.y(), weights_shape.x(),</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  weights_shape.y(), conv_info);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input_shape };</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <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="l00120"></a><span class="lineno"> 120</span>  <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="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <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="l00123"></a><span class="lineno"> 123</span> }</div><div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#aa05837651dbee5634edf3da2cb8532c9"> 124</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#aa05837651dbee5634edf3da2cb8532c9">compute_deconvolution_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> sx, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> sy, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inner_border_right, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inner_border_top, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>)</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> {</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> scale_out_shape(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>());</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_x = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) + (input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) - 1) * (sx - 1) + inner_border_right + 2 * info.<a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml#a9a9d6d62752247f733a3466b484e08b9">pad</a>().first;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_y = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) + (input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) - 1) * (sy - 1) + inner_border_top + 2 * info.<a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml#a9a9d6d62752247f733a3466b484e08b9">pad</a>().second;</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  scale_out_shape.set(0, out_x);</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  scale_out_shape.set(1, out_y);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordflow">return</span> scale_out_shape;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> }</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> } <span class="comment">// namespace shape_calculator</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> } <span class="comment">// namespace misc</span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> } <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> <span class="preprocessor">#endif </span><span class="comment">/* __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__ */</span><span class="preprocessor"></span></div><div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a1f4e725b8e1ea36b30e09dc08ae6961d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">arm_compute::ITensorInfo::num_dimensions</a></div><div class="ttdeci">virtual size_t num_dimensions() const =0</div><div class="ttdoc">The number of dimensions of the tensor (rank) </div></div>
-<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00038">TensorShape.h:38</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a593fb7ecc281425b190cd6f20164b1a3"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a593fb7ecc281425b190cd6f20164b1a3">arm_compute::misc::shape_calculator::compute_permutation_output_shape</a></div><div class="ttdeci">TensorShape compute_permutation_output_shape(const ITensorInfo &input, const PermutationVector &perm)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00036">ShapeCalculator.h:36</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_acb74edf42335de0dca0da5158b704c4b"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">arm_compute::TensorShape::remove_dimension</a></div><div class="ttdeci">void remove_dimension(size_t n)</div><div class="ttdoc">Accessor to remove the dimension n from the tensor shape. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00101">TensorShape.h:101</a></div></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#l00118">GEMM.cpp:118</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml_a9a9d6d62752247f733a3466b484e08b9"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml#a9a9d6d62752247f733a3466b484e08b9">arm_compute::PadStrideInfo::pad</a></div><div class="ttdeci">std::pair< unsigned int, unsigned int > pad() const </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00516">Types.h:516</a></div></div>
+<a href="_shape_calculator_8h.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017-2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#ifndef __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#define __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_helpers_8h.xhtml">arm_compute/core/Helpers.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_i_tensor_info_8h.xhtml">arm_compute/core/ITensorInfo.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_utils_8h.xhtml">arm_compute/core/Utils.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> {</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="keyword">namespace </span>misc</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> {</div><div class="line"><a name="l00035"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml"> 35</a></span> <span class="keyword">namespace </span>shape_calculator</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> {</div><div class="line"><a name="l00037"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a593fb7ecc281425b190cd6f20164b1a3"> 37</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a593fb7ecc281425b190cd6f20164b1a3">compute_permutation_output_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_strides.xhtml">PermutationVector</a> &perm)</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> {</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <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>  <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> }</div><div class="line"><a name="l00043"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3c26be6728535f0d86399791dee71132"> 43</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3c26be6728535f0d86399791dee71132">compute_weights_reshaped_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &weights, <span class="keywordtype">bool</span> has_bias = <span class="keyword">false</span>)</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> {</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="comment">// Calculate output shape</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> weights_reshaped{ weights.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  weights_reshaped.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">collapse</a>(3);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <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>  weights_reshaped.set(0, weights_reshaped[1]);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  weights_reshaped.set(1, tmp_dim + (has_bias ? 1 : 0));</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">return</span> weights_reshaped;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> }</div><div class="line"><a name="l00054"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#abda73d5a6ce1a003535523fc1111c1c2"> 54</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#abda73d5a6ce1a003535523fc1111c1c2">compute_interleaved_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>, <span class="keywordtype">int</span> mult_interleave4x4_height = 1)</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <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>  <a class="code" href="core_2_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(mult_interleave4x4_height < 1);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <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>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_interleaved_a{ a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  shape_interleaved_a.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">set</a>(0, a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) * interleave_width);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  shape_interleaved_a.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">set</a>(1, std::ceil(a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) / <span class="keyword">static_cast<</span><span class="keywordtype">float</span><span class="keyword">></span>(interleave_width)));</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">return</span> shape_interleaved_a;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> }</div><div class="line"><a name="l00065"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a70a2ef9fd754b5798a0a92656f8b5fcf"> 65</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a70a2ef9fd754b5798a0a92656f8b5fcf">compute_transpose1xW_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>)</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> {</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <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>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_transposed1xW_b{ b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  shape_transposed1xW_b.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">set</a>(0, b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) * 16);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  shape_transposed1xW_b.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">set</a>(1, std::ceil(b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) / 16.f));</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> </div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordflow">return</span> shape_transposed1xW_b;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> }</div><div class="line"><a name="l00074"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5797726a8fbee3b11b92757c2f0031d6"> 74</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5797726a8fbee3b11b92757c2f0031d6">compute_transpose1xW_with_element_size_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>, <span class="keywordtype">int</span> mult_transpose1xW_width = 1)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> {</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <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>  <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>  <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>  <a class="code" href="core_2_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(mult_transpose1xW_width < 1);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_transposed1xW_b{ b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> transpose_width = (16 / b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">element_size</a>()) * mult_transpose1xW_width;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  shape_transposed1xW_b.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">set</a>(0, b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) * transpose_width);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  shape_transposed1xW_b.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">set</a>(1, static_cast<size_t>(std::ceil(b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) / <span class="keyword">static_cast<</span><span class="keywordtype">float</span><span class="keyword">></span>(transpose_width))));</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordflow">return</span> shape_transposed1xW_b;</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> }</div><div class="line"><a name="l00087"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a60ce6c017f70d978b48b101ce314969e"> 87</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a60ce6c017f70d978b48b101ce314969e">compute_reductionA_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> {</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_vector_sum_col{ b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordflow">if</span>(shape_vector_sum_col.num_dimensions() > 1)</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  {</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  shape_vector_sum_col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">remove_dimension</a>(1);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  }</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordflow">return</span> shape_vector_sum_col;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> }</div><div class="line"><a name="l00097"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69f9b3191aafc4905f9d029ff9d48fea"> 97</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69f9b3191aafc4905f9d029ff9d48fea">compute_reductionB_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>)</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> {</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_vector_sum_row{ a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  shape_vector_sum_row.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1));</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">if</span>(a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1)</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  {</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  shape_vector_sum_row.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">remove_dimension</a>(1);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  }</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keywordflow">return</span> shape_vector_sum_row;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> }</div><div class="line"><a name="l00108"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a198e3937aafccfdf3cb6327a87fc1aec"> 108</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a198e3937aafccfdf3cb6327a87fc1aec">compute_im2col_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input)</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> {</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_im2col{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">collapse</a>(3);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keywordflow">return</span> shape_im2col;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> }</div><div class="line"><a name="l00115"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a560bd8b86a795d8db099ddddaa6df41b"> 115</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a560bd8b86a795d8db099ddddaa6df41b">compute_col2im_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, std::pair<unsigned int, unsigned int> convolved_dims)</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> {</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> col2im_shape{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  col2im_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">set</a>(0, convolved_dims.first);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  col2im_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">set</a>(1, convolved_dims.second);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  col2im_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">set</a>(2, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[0]);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordflow">return</span> col2im_shape;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> }</div><div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf"> 124</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input)</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> {</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_transposed{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  shape_transposed.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">set</a>(0, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1));</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  shape_transposed.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">set</a>(1, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0));</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">return</span> shape_transposed;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> }</div><div class="line"><a name="l00133"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8cf251400728a619fd1026788988da34"> 133</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8cf251400728a619fd1026788988da34">compute_depthwise_convolution_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &weights, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_info)</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> {</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> input_shape{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> weights_shape{ weights.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <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>  <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>  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>  weights_shape.x(), weights_shape.y(),</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  conv_info);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> </div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <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>  <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>  <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> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <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> }</div><div class="line"><a name="l00150"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#aa05837651dbee5634edf3da2cb8532c9"> 150</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#aa05837651dbee5634edf3da2cb8532c9">compute_deconvolution_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> sx, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> sy, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inner_border_right, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inner_border_top, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>)</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> {</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> scale_out_shape(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>());</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_x = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) + (input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) - 1) * (sx - 1) + inner_border_right + 2 * info.<a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml#a9a9d6d62752247f733a3466b484e08b9">pad</a>().first;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_y = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) + (input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) - 1) * (sy - 1) + inner_border_top + 2 * info.<a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml#a9a9d6d62752247f733a3466b484e08b9">pad</a>().second;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  scale_out_shape.set(0, out_x);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  scale_out_shape.set(1, out_y);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordflow">return</span> scale_out_shape;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> }</div><div class="line"><a name="l00160"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a7585cd5e5c5b7f1146760396ce7fa540"> 160</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a198e3937aafccfdf3cb6327a87fc1aec">compute_im2col_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <span class="keywordtype">int</span> num_input_dimensions = 3)</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> {</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> </div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <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> </div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <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> }</div><div class="line"><a name="l00168"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac18940223de1db7f6ed6c49119be7cd8"> 168</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac18940223de1db7f6ed6c49119be7cd8">compute_interleave_custom_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &input, <span class="keyword">const</span> <span class="keywordtype">int</span> x_interleave, <span class="keyword">const</span> <span class="keywordtype">int</span> y_interleave)</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> {</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <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> </div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <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>  <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<</span><span class="keywordtype">float</span><span class="keyword">></span>(y_interleave)));</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <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> }</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> </div><div class="line"><a name="l00178"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a734391819bbd2b0fa8400c06b7956d9e"> 178</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a734391819bbd2b0fa8400c06b7956d9e">compute_fully_connected_reshaped_weights_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keywordtype">bool</span> transpose_weights, <span class="keywordtype">bool</span> is_batched_fc_layer, <span class="keyword">const</span> <span class="keywordtype">int</span> interleave)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> {</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="comment">// Transpose weights if the user hasn't done it</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">if</span>(transpose_weights)</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  {</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <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>  }</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> </div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <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>  <span class="keywordflow">if</span>(is_batched_fc_layer)</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <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-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->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>  <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>  }</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="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> }</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> } <span class="comment">// namespace shape_calculator</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> } <span class="comment">// namespace misc</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> } <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> <span class="preprocessor">#endif </span><span class="comment">/* __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__ */</span><span class="preprocessor"></span></div><div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a1f4e725b8e1ea36b30e09dc08ae6961d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">arm_compute::ITensorInfo::num_dimensions</a></div><div class="ttdeci">virtual size_t num_dimensions() const =0</div><div class="ttdoc">The number of dimensions of the tensor (rank) </div></div>
+<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a593fb7ecc281425b190cd6f20164b1a3"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a593fb7ecc281425b190cd6f20164b1a3">arm_compute::misc::shape_calculator::compute_permutation_output_shape</a></div><div class="ttdeci">TensorShape compute_permutation_output_shape(const ITensorInfo &input, const PermutationVector &perm)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00037">ShapeCalculator.h:37</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_acb74edf42335de0dca0da5158b704c4b"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">arm_compute::TensorShape::remove_dimension</a></div><div class="ttdeci">void remove_dimension(size_t n)</div><div class="ttdoc">Accessor to remove the dimension n from the tensor shape. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00106">TensorShape.h:106</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a5797726a8fbee3b11b92757c2f0031d6"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5797726a8fbee3b11b92757c2f0031d6">arm_compute::misc::shape_calculator::compute_transpose1xW_with_element_size_shape</a></div><div class="ttdeci">TensorShape compute_transpose1xW_with_element_size_shape(const ITensorInfo &b, int mult_transpose1xW_width=1)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00074">ShapeCalculator.h:74</a></div></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#l00119">GEMM.cpp:119</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml_a9a9d6d62752247f733a3466b484e08b9"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml#a9a9d6d62752247f733a3466b484e08b9">arm_compute::PadStrideInfo::pad</a></div><div class="ttdeci">std::pair< unsigned int, unsigned int > pad() const </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00543">Types.h:543</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension. </div></div>
-<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a60ce6c017f70d978b48b101ce314969e"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a60ce6c017f70d978b48b101ce314969e">arm_compute::misc::shape_calculator::compute_reductionA_shape</a></div><div class="ttdeci">TensorShape compute_reductionA_shape(const ITensorInfo &b)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00071">ShapeCalculator.h:71</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml">arm_compute::ITensorInfo</a></div><div class="ttdoc">Store the tensor&#39;s metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_info_8h_source.xhtml#l00039">ITensorInfo.h:39</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_abda73d5a6ce1a003535523fc1111c1c2"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#abda73d5a6ce1a003535523fc1111c1c2">arm_compute::misc::shape_calculator::compute_interleaved_shape</a></div><div class="ttdeci">TensorShape compute_interleaved_shape(const ITensorInfo &a, int mult_interleave4x4_height=1)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00054">ShapeCalculator.h:54</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>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a60ce6c017f70d978b48b101ce314969e"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a60ce6c017f70d978b48b101ce314969e">arm_compute::misc::shape_calculator::compute_reductionA_shape</a></div><div class="ttdeci">TensorShape compute_reductionA_shape(const ITensorInfo &b)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00087">ShapeCalculator.h:87</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml">arm_compute::ITensorInfo</a></div><div class="ttdoc">Store the tensor&#39;s metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_info_8h_source.xhtml#l00040">ITensorInfo.h:40</a></div></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 &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_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() -> set_format(Format::S16)</div></div>
<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">This file contains all available output stages for GEMMLowp on OpenCL. </div><div class="ttdef"><b>Definition:</b> <a href="01__library_8dox_source.xhtml#l00001">01_library.dox:1</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 &input)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00099">ShapeCalculator.h:99</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a3c26be6728535f0d86399791dee71132"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3c26be6728535f0d86399791dee71132">arm_compute::misc::shape_calculator::compute_weights_reshaped_shape</a></div><div class="ttdeci">TensorShape compute_weights_reshaped_shape(const ITensorInfo &weights, bool has_bias=false)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00043">ShapeCalculator.h:43</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 &input)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00124">ShapeCalculator.h:124</a></div></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< T > &dimensions, const PermutationVector &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>
-<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_aa05837651dbee5634edf3da2cb8532c9"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#aa05837651dbee5634edf3da2cb8532c9">arm_compute::misc::shape_calculator::compute_deconvolution_shape</a></div><div class="ttdeci">TensorShape compute_deconvolution_shape(const ITensorInfo &input, unsigned int sx, unsigned int sy, unsigned int inner_border_right, unsigned int inner_border_top, const PadStrideInfo &info)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00124">ShapeCalculator.h:124</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_aa05837651dbee5634edf3da2cb8532c9"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#aa05837651dbee5634edf3da2cb8532c9">arm_compute::misc::shape_calculator::compute_deconvolution_shape</a></div><div class="ttdeci">TensorShape compute_deconvolution_shape(const ITensorInfo &input, unsigned int sx, unsigned int sy, unsigned int inner_border_right, unsigned int inner_border_top, const PadStrideInfo &info)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00150">ShapeCalculator.h:150</a></div></div>
<div class="ttc" id="arm__compute_2core_2_utils_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_utils_8h.xhtml">Utils.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor. </div></div>
-<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a8cf251400728a619fd1026788988da34"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8cf251400728a619fd1026788988da34">arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape</a></div><div class="ttdeci">TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, PadStrideInfo conv_info)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00108">ShapeCalculator.h:108</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#l00112">GEMM.cpp:112</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a68eacb752d795a850ba6bfe7b226f29c"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a68eacb752d795a850ba6bfe7b226f29c">arm_compute::misc::shape_calculator::compute_transpose1xW_with_element_size_shape</a></div><div class="ttdeci">TensorShape compute_transpose1xW_with_element_size_shape(const ITensorInfo &b)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00060">ShapeCalculator.h:60</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a8cf251400728a619fd1026788988da34"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8cf251400728a619fd1026788988da34">arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape</a></div><div class="ttdeci">TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, PadStrideInfo conv_info)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00133">ShapeCalculator.h:133</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#l00113">GEMM.cpp:113</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a560bd8b86a795d8db099ddddaa6df41b"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a560bd8b86a795d8db099ddddaa6df41b">arm_compute::misc::shape_calculator::compute_col2im_shape</a></div><div class="ttdeci">TensorShape compute_col2im_shape(const ITensorInfo &input, std::pair< unsigned int, unsigned int > convolved_dims)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00115">ShapeCalculator.h:115</a></div></div>
+<div class="ttc" id="classarm__compute_1_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr< T > clone() const =0</div><div class="ttdoc">Provide a clone of the current object of class T. </div></div>
<div class="ttc" id="_i_tensor_info_8h_xhtml"><div class="ttname"><a href="_i_tensor_info_8h.xhtml">ITensorInfo.h</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00468">Types.h:468</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00491">Types.h:491</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_aa459796b5489eca8a9160cb5dcf1a103"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">arm_compute::ITensorInfo::element_size</a></div><div class="ttdeci">virtual size_t element_size() const =0</div><div class="ttdoc">Element size in bytes calculated as data_size() * num_channels() </div></div>
-<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a0cb0e1f5da2e1cc2e0ea5690450f53e8"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a0cb0e1f5da2e1cc2e0ea5690450f53e8">arm_compute::TensorShape::set</a></div><div class="ttdeci">void set(size_t dimension, size_t value)</div><div class="ttdoc">Accessor to set the value of one of the dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00074">TensorShape.h:74</a></div></div>
<div class="ttc" id="classarm__compute_1_1_strides_xhtml"><div class="ttname"><a href="classarm__compute_1_1_strides.xhtml">arm_compute::Strides</a></div><div class="ttdoc">Strides of an item in bytes. </div><div class="ttdef"><b>Definition:</b> <a href="_strides_8h_source.xhtml#l00037">Strides.h:37</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a69f9b3191aafc4905f9d029ff9d48fea"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69f9b3191aafc4905f9d029ff9d48fea">arm_compute::misc::shape_calculator::compute_reductionB_shape</a></div><div class="ttdeci">TensorShape compute_reductionB_shape(const ITensorInfo &a)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00081">ShapeCalculator.h:81</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a198e3937aafccfdf3cb6327a87fc1aec"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a198e3937aafccfdf3cb6327a87fc1aec">arm_compute::misc::shape_calculator::compute_im2col_shape</a></div><div class="ttdeci">TensorShape compute_im2col_shape(const ITensorInfo &input)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00092">ShapeCalculator.h:92</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a734391819bbd2b0fa8400c06b7956d9e"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a734391819bbd2b0fa8400c06b7956d9e">arm_compute::misc::shape_calculator::compute_fully_connected_reshaped_weights_shape</a></div><div class="ttdeci">TensorShape compute_fully_connected_reshaped_weights_shape(const ITensorInfo *input, bool transpose_weights, bool is_batched_fc_layer, const int interleave)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00178">ShapeCalculator.h:178</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a69f9b3191aafc4905f9d029ff9d48fea"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69f9b3191aafc4905f9d029ff9d48fea">arm_compute::misc::shape_calculator::compute_reductionB_shape</a></div><div class="ttdeci">TensorShape compute_reductionB_shape(const ITensorInfo &a)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00097">ShapeCalculator.h:97</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a3095e0ccbbc39daf5b3816193edca6ad"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a3095e0ccbbc39daf5b3816193edca6ad">arm_compute::TensorShape::set</a></div><div class="ttdeci">TensorShape & set(size_t dimension, size_t value)</div><div class="ttdoc">Accessor to set the value of one of the dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00077">TensorShape.h:77</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a198e3937aafccfdf3cb6327a87fc1aec"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a198e3937aafccfdf3cb6327a87fc1aec">arm_compute::misc::shape_calculator::compute_im2col_shape</a></div><div class="ttdeci">TensorShape compute_im2col_shape(const ITensorInfo &input)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00108">ShapeCalculator.h:108</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< unsigned int, unsigned int > scaled_dimensions(unsigned int width, unsigned int height, unsigned int kernel_width, unsigned int kernel_height, const PadStrideInfo &pad_stride_info)</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_1_1misc_1_1shape__calculator_xhtml_a70a2ef9fd754b5798a0a92656f8b5fcf"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a70a2ef9fd754b5798a0a92656f8b5fcf">arm_compute::misc::shape_calculator::compute_transpose1xW_shape</a></div><div class="ttdeci">TensorShape compute_transpose1xW_shape(const ITensorInfo &b)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00051">ShapeCalculator.h:51</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a4bf459b95015d89d6fccb22c15f3b4f5"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a4bf459b95015d89d6fccb22c15f3b4f5">arm_compute::misc::shape_calculator::compute_interleaved_shape</a></div><div class="ttdeci">TensorShape compute_interleaved_shape(const ITensorInfo &a)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00042">ShapeCalculator.h:42</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#l00119">GEMM.cpp:119</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a8e15e87871211f98c2b566137e38ef99"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">arm_compute::TensorShape::collapse</a></div><div class="ttdeci">void collapse(size_t n, size_t first=0)</div><div class="ttdoc">Collapse the first n dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00123">TensorShape.h:123</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a70a2ef9fd754b5798a0a92656f8b5fcf"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a70a2ef9fd754b5798a0a92656f8b5fcf">arm_compute::misc::shape_calculator::compute_transpose1xW_shape</a></div><div class="ttdeci">TensorShape compute_transpose1xW_shape(const ITensorInfo &b)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00065">ShapeCalculator.h:65</a></div></div>
+<div class="ttc" id="arm__compute_2core_2_helpers_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_helpers_8h.xhtml">Helpers.h</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#l00120">GEMM.cpp:120</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a8e15e87871211f98c2b566137e38ef99"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">arm_compute::TensorShape::collapse</a></div><div class="ttdeci">void collapse(size_t n, size_t first=0)</div><div class="ttdoc">Collapse the first n dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00128">TensorShape.h:128</a></div></div>
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<li class="navelem"><a class="el" href="dir_214608ef36d61442cb2b0c1c4e9a7def.xhtml">arm_compute</a></li><li class="navelem"><a class="el" href="dir_1fb090f0c6070330bfaccc4236d3ca0d.xhtml">core</a></li><li class="navelem"><a class="el" href="dir_7b0825b7536eda12b63e5d87347b1c7d.xhtml">utils</a></li><li class="navelem"><a class="el" href="dir_cab437ca4f6ec1fbc573ae83f91b2654.xhtml">misc</a></li><li class="navelem"><a class="el" href="_shape_calculator_8h.xhtml">ShapeCalculator.h</a></li>
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projects / platform / upstream / armcl.git / blobdiff