arm_compute v17.09
Change-Id: I4bf8f4e6e5f84ce0d5b6f5ba570d276879f42a81
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<title>Compute Library: examples/neon_copy_objects.cpp File Reference</title>
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<div id="projectname">Compute Library
-  <span id="projectnumber">v17.06</span>
+  <span id="projectnumber">17.09</span>
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<div class="title">neon_copy_objects.cpp File Reference</div> </div>
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<div class="contents">
-<div class="textblock"><code>#include "<a class="el" href="_n_e_functions_8h_source.xhtml">arm_compute/runtime/NEON/NEFunctions.h</a>"</code><br />
-<code>#include "<a class="el" href="arm__compute_2core_2_types_8h_source.xhtml">arm_compute/core/Types.h</a>"</code><br />
-<code>#include "<a class="el" href="utils_2_utils_8h_source.xhtml">utils/Utils.h</a>"</code><br />
-<code>#include <cstring></code><br />
-<code>#include <iostream></code><br />
+<div class="textblock"><code>#include "<a class="el" href="_n_e_functions_8h_source.xhtml">arm_compute/runtime/NEON/NEFunctions.h</a>"</code><br/>
+<code>#include "<a class="el" href="arm__compute_2core_2_types_8h_source.xhtml">arm_compute/core/Types.h</a>"</code><br/>
+<code>#include "<a class="el" href="utils_2_utils_8h_source.xhtml">utils/Utils.h</a>"</code><br/>
+<code>#include <cstring></code><br/>
+<code>#include <iostream></code><br/>
</div>
<p><a href="neon__copy__objects_8cpp_source.xhtml">Go to the source code of this file.</a></p>
<table class="memberdecls">
@@ -132,7 +130,7 @@
<tr class="memitem:a548cd646528b7a0644cb08e483e7ee2b"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="neon__copy__objects_8cpp.xhtml#a548cd646528b7a0644cb08e483e7ee2b">main_neon_copy_objects</a> (int argc, const char **argv)</td></tr>
<tr class="separator:a548cd646528b7a0644cb08e483e7ee2b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a217dbf8b442f20279ea00b898af96f52"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="neon__copy__objects_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52">main</a> (int argc, const char **argv)</td></tr>
-<tr class="memdesc:a217dbf8b442f20279ea00b898af96f52"><td class="mdescLeft"> </td><td class="mdescRight">Main program for the copy objects test. <a href="#a217dbf8b442f20279ea00b898af96f52">More...</a><br /></td></tr>
+<tr class="memdesc:a217dbf8b442f20279ea00b898af96f52"><td class="mdescLeft"> </td><td class="mdescRight">Main program for the copy objects test. <a href="#a217dbf8b442f20279ea00b898af96f52">More...</a><br/></td></tr>
<tr class="separator:a217dbf8b442f20279ea00b898af96f52"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<h2 class="groupheader">Function Documentation</h2>
@@ -172,7 +170,10 @@
<p>Definition at line <a class="el" href="neon__copy__objects_8cpp_source.xhtml#l00149">149</a> of file <a class="el" href="neon__copy__objects_8cpp_source.xhtml">neon_copy_objects.cpp</a>.</p>
<p>References <a class="el" href="neon__copy__objects_8cpp_source.xhtml#l00035">main_neon_copy_objects()</a>, and <a class="el" href="utils_2_utils_8cpp_source.xhtml#l00069">arm_compute::utils::run_example()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> {</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1utils.xhtml#a4c9395db2c8b8d0c336656a7b58fca3e">utils::run_example</a>(argc, argv, <a class="code" href="neon__copy__objects_8cpp.xhtml#a548cd646528b7a0644cb08e483e7ee2b">main_neon_copy_objects</a>);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> }</div><div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a4c9395db2c8b8d0c336656a7b58fca3e"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a4c9395db2c8b8d0c336656a7b58fca3e">arm_compute::utils::run_example</a></div><div class="ttdeci">int run_example(int argc, const char **argv, example &func)</div><div class="ttdoc">Run an example and handle the potential exceptions it throws. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8cpp_source.xhtml#l00069">Utils.cpp:69</a></div></div>
+<div class="fragment"><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> {</div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1utils.xhtml#a4c9395db2c8b8d0c336656a7b58fca3e">utils::run_example</a>(argc, argv, <a class="code" href="neon__copy__objects_8cpp.xhtml#a548cd646528b7a0644cb08e483e7ee2b">main_neon_copy_objects</a>);</div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a4c9395db2c8b8d0c336656a7b58fca3e"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a4c9395db2c8b8d0c336656a7b58fca3e">arm_compute::utils::run_example</a></div><div class="ttdeci">int run_example(int argc, const char **argv, example &func)</div><div class="ttdoc">Run an example and handle the potential exceptions it throws. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8cpp_source.xhtml#l00069">Utils.cpp:69</a></div></div>
<div class="ttc" id="neon__copy__objects_8cpp_xhtml_a548cd646528b7a0644cb08e483e7ee2b"><div class="ttname"><a href="neon__copy__objects_8cpp.xhtml#a548cd646528b7a0644cb08e483e7ee2b">main_neon_copy_objects</a></div><div class="ttdeci">void main_neon_copy_objects(int argc, const char **argv)</div><div class="ttdef"><b>Definition:</b> <a href="neon__copy__objects_8cpp_source.xhtml#l00035">neon_copy_objects.cpp:35</a></div></div>
</div><!-- fragment -->
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@@ -205,30 +206,137 @@
<p>Definition at line <a class="el" href="neon__copy__objects_8cpp_source.xhtml#l00035">35</a> of file <a class="el" href="neon__copy__objects_8cpp_source.xhtml">neon_copy_objects.cpp</a>.</p>
-<p>References <a class="el" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">TensorAllocator::allocate()</a>, <a class="el" href="classarm__compute_1_1_tensor.xhtml#a531ec877bfc923dea3ab6f1be5e6e1ac">Tensor::allocator()</a>, <a class="el" href="_error_8h_source.xhtml#l00049">ARM_COMPUTE_UNUSED</a>, <a class="el" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#a9daf8026e68559806afe7d0aa12693d6">NESoftmaxLayer::configure()</a>, <a class="el" href="_window_8h_source.xhtml#l00045">Window::DimY</a>, <a class="el" href="_window_8h_source.xhtml#l00090">Window::Dimension::end()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00176">arm_compute::execute_window_loop()</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="classarm__compute_1_1_tensor.xhtml#aa68535e0983cc60a425253a72b162661">Tensor::info()</a>, <a class="el" href="classarm__compute_1_1_tensor_allocator.xhtml#a3014ce2f4215e8a44331aa5daf3ba0d4">TensorAllocator::init()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00232">Iterator::ptr()</a>, <a class="el" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">NESoftmaxLayer::run()</a>, <a class="el" href="_window_8h_source.xhtml#l00085">Window::Dimension::start()</a>, <a class="el" href="_window_8h_source.xhtml#l00095">Window::Dimension::step()</a>, <a class="el" href="_window_8inl_source.xhtml#l00175">Window::use_tensor_dimensions()</a>, <a class="el" href="_window_8h_source.xhtml#l00128">Window::x()</a>, <a class="el" href="_window_8h_source.xhtml#l00137">Window::y()</a>, and <a class="el" href="_window_8h_source.xhtml#l00146">Window::z()</a>.</p>
+<p>References <a class="el" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">TensorAllocator::allocate()</a>, <a class="el" href="classarm__compute_1_1_tensor.xhtml#a531ec877bfc923dea3ab6f1be5e6e1ac">Tensor::allocator()</a>, <a class="el" href="_error_8h_source.xhtml#l00049">ARM_COMPUTE_UNUSED</a>, <a class="el" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#a9daf8026e68559806afe7d0aa12693d6">NESoftmaxLayer::configure()</a>, <a class="el" href="_window_8h_source.xhtml#l00045">Window::DimY</a>, <a class="el" href="_window_8h_source.xhtml#l00090">Window::Dimension::end()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00127">arm_compute::execute_window_loop()</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="classarm__compute_1_1_tensor.xhtml#aa68535e0983cc60a425253a72b162661">Tensor::info()</a>, <a class="el" href="classarm__compute_1_1_tensor_allocator.xhtml#a3014ce2f4215e8a44331aa5daf3ba0d4">TensorAllocator::init()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00183">Iterator::ptr()</a>, <a class="el" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">NESoftmaxLayer::run()</a>, <a class="el" href="_c_l_2_min_max_location_8cpp_source.xhtml#l00089">arm_compute::test::validation::shape</a>, <a class="el" href="_window_8h_source.xhtml#l00085">Window::Dimension::start()</a>, <a class="el" href="_window_8h_source.xhtml#l00095">Window::Dimension::step()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_window_8inl_source.xhtml#l00204">Window::use_tensor_dimensions()</a>, <a class="el" href="_window_8h_source.xhtml#l00128">Window::x()</a>, <a class="el" href="_window_8h_source.xhtml#l00137">Window::y()</a>, and <a class="el" href="_window_8h_source.xhtml#l00146">Window::z()</a>.</p>
<p>Referenced by <a class="el" href="neon__copy__objects_8cpp_source.xhtml#l00149">main()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <a class="code" href="_error_8h.xhtml#a4103adbb45806b2f2002d44b91d0d206">ARM_COMPUTE_UNUSED</a>(argc);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <a class="code" href="_error_8h.xhtml#a4103adbb45806b2f2002d44b91d0d206">ARM_COMPUTE_UNUSED</a>(argv);</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 4;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 3;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batch = 2;</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="keyword">auto</span> *src_data = <span class="keyword">new</span> <span class="keywordtype">float</span>[width * height * batch];</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">auto</span> *dst_data = <span class="keyword">new</span> <span class="keywordtype">float</span>[width * height * batch];</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// Fill src_data with dummy values:</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> b = 0; b < batch; b++)</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> h = 0; h < height; h++)</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="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w < width; w++)</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  src_data[b * (width * height) + h * width + w] = static_cast<float>(100 * b + 10 * h + w);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  }</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>  }</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="classarm__compute_1_1_tensor.xhtml">Tensor</a> input, output;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="classarm__compute_1_1_n_e_softmax_layer.xhtml">NESoftmaxLayer</a> softmax;</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="comment">// Initialize the tensors dimensions and type:</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape(width, height, batch);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  input.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a531ec877bfc923dea3ab6f1be5e6e1ac">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3014ce2f4215e8a44331aa5daf3ba0d4">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(shape, 1, DataType::F32));</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a531ec877bfc923dea3ab6f1be5e6e1ac">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3014ce2f4215e8a44331aa5daf3ba0d4">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(shape, 1, DataType::F32));</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="comment">// Configure softmax:</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  softmax.<a class="code" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#a9daf8026e68559806afe7d0aa12693d6">configure</a>(&input, &output);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="comment">// Allocate the input / output tensors:</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  input.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a531ec877bfc923dea3ab6f1be5e6e1ac">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a531ec877bfc923dea3ab6f1be5e6e1ac">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="comment">// Fill the input tensor:</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="comment">// Simplest way: create an iterator to iterate through each element of the input tensor:</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> input_window;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#a5bad22e0142f7e50f9a3005ddd982d8f">use_tensor_dimensions</a>(input.<a class="code" href="classarm__compute_1_1_tensor.xhtml#aa68535e0983cc60a425253a72b162661">info</a>());</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  std::cout << <span class="stringliteral">" Dimensions of the input's iterator:\n"</span>;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  std::cout << <span class="stringliteral">" X = [start="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">start</a>() << <span class="stringliteral">", end="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">end</a>() << <span class="stringliteral">", step="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">step</a>() << <span class="stringliteral">"]\n"</span>;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  std::cout << <span class="stringliteral">" Y = [start="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">start</a>() << <span class="stringliteral">", end="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">end</a>() << <span class="stringliteral">", step="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">step</a>() << <span class="stringliteral">"]\n"</span>;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  std::cout << <span class="stringliteral">" Z = [start="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">z</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">start</a>() << <span class="stringliteral">", end="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">z</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">end</a>() << <span class="stringliteral">", step="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">z</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">step</a>() << <span class="stringliteral">"]\n"</span>;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="comment">// Create an iterator:</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> input_it(&input, input_window);</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>  <span class="comment">// Iterate through the elements of src_data and copy them one by one to the input tensor:</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="comment">// This is equivalent to:</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="comment">// for( unsigned int z = 0; z < batch; ++z)</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="comment">// {</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="comment">// for( unsigned int y = 0; y < height; ++y)</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="comment">// {</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="comment">// for( unsigned int x = 0; x < width; ++x)</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="comment">// {</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="comment">// *reinterpret_cast<float*>( input.buffer() + input.info()->offset_element_in_bytes(Coordinates(x,y,z))) = src_data[ z * (width*height) + y * width + x];</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="comment">// }</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="comment">// }</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="comment">// }</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="comment">// Except it works for an arbitrary number of dimensions</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(input_window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  std::cout << <span class="stringliteral">"Setting item ["</span> << <span class="keywordtype">id</span>.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>() << <span class="stringliteral">","</span> << <span class="keywordtype">id</span>.y() << <span class="stringliteral">","</span> << <span class="keywordtype">id</span>.z() << <span class="stringliteral">"]\n"</span>;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  *<span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span> *<span class="keyword">></span>(input_it.ptr()) = src_data[<span class="keywordtype">id</span>.z() * (width * height) + <span class="keywordtype">id</span>.y() * width + <span class="keywordtype">id</span>.x()];</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>  input_it);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="comment">// Run NEON softmax:</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  softmax.<a class="code" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</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="comment">// More efficient way: create an iterator to iterate through each row (instead of each element) of the output tensor:</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> output_window;</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#a5bad22e0142f7e50f9a3005ddd982d8f">use_tensor_dimensions</a>(output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#aa68535e0983cc60a425253a72b162661">info</a>(), <span class="comment">/* first_dimension =*/</span>Window::DimY); <span class="comment">// Iterate through the rows (not each element)</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  std::cout << <span class="stringliteral">" Dimensions of the output's iterator:\n"</span>;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  std::cout << <span class="stringliteral">" X = [start="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">start</a>() << <span class="stringliteral">", end="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">end</a>() << <span class="stringliteral">", step="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">step</a>() << <span class="stringliteral">"]\n"</span>;</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  std::cout << <span class="stringliteral">" Y = [start="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">start</a>() << <span class="stringliteral">", end="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">end</a>() << <span class="stringliteral">", step="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">step</a>() << <span class="stringliteral">"]\n"</span>;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  std::cout << <span class="stringliteral">" Z = [start="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">z</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">start</a>() << <span class="stringliteral">", end="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">z</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">end</a>() << <span class="stringliteral">", step="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">z</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">step</a>() << <span class="stringliteral">"]\n"</span>;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="comment">// Create an iterator:</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> output_it(&output, output_window);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="comment">// Iterate through the rows of the output tensor and copy them to dst_data:</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="comment">// This is equivalent to:</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="comment">// for( unsigned int z = 0; z < batch; ++z)</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="comment">// {</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="comment">// for( unsigned int y = 0; y < height; ++y)</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="comment">// {</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="comment">// memcpy( dst_data + z * (width*height) + y * width, input.buffer() + input.info()->offset_element_in_bytes(Coordinates(0,y,z)), width * sizeof(float));</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="comment">// }</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// }</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="comment">// Except it works for an arbitrary number of dimensions</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(output_window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  std::cout << <span class="stringliteral">"Copying one row starting from ["</span> << <span class="keywordtype">id</span>.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>() << <span class="stringliteral">","</span> << <span class="keywordtype">id</span>.y() << <span class="stringliteral">","</span> << <span class="keywordtype">id</span>.z() << <span class="stringliteral">"]\n"</span>;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">// Copy one whole row:</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  memcpy(dst_data + <span class="keywordtype">id</span>.z() * (width * height) + <span class="keywordtype">id</span>.y() * width, output_it.ptr(), width * <span class="keyword">sizeof</span>(float));</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  },</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  output_it);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">delete</span>[] src_data;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keyword">delete</span>[] dst_data;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> }</div><div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00038">TensorShape.h:38</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_window_xhtml_a5bad22e0142f7e50f9a3005ddd982d8f"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a5bad22e0142f7e50f9a3005ddd982d8f">arm_compute::Window::use_tensor_dimensions</a></div><div class="ttdeci">void use_tensor_dimensions(const ITensorInfo *info, size_t first_dimension=Window::DimX)</div><div class="ttdoc">Use the tensor&#39;s dimensions to fill the window dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00175">Window.inl:175</a></div></div>
+<div class="fragment"><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> {</div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <a class="code" href="_error_8h.xhtml#a4103adbb45806b2f2002d44b91d0d206">ARM_COMPUTE_UNUSED</a>(argc);</div>
+<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <a class="code" href="_error_8h.xhtml#a4103adbb45806b2f2002d44b91d0d206">ARM_COMPUTE_UNUSED</a>(argv);</div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div>
+<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 4;</div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 3;</div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batch = 2;</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="keyword">auto</span> *src_data = <span class="keyword">new</span> <span class="keywordtype">float</span>[width * height * batch];</div>
+<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">auto</span> *dst_data = <span class="keyword">new</span> <span class="keywordtype">float</span>[width * height * batch];</div>
+<div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div>
+<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// Fill src_data with dummy values:</span></div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> b = 0; b < batch; b++)</div>
+<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  {</div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> h = 0; h < height; h++)</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="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w < width; w++)</div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  {</div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  src_data[b * (width * height) + h * width + w] = static_cast<float>(100 * b + 10 * h + w);</div>
+<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  }</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>  }</div>
+<div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div>
+<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="classarm__compute_1_1_tensor.xhtml">Tensor</a> input, output;</div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="classarm__compute_1_1_n_e_softmax_layer.xhtml">NESoftmaxLayer</a> softmax;</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="comment">// Initialize the tensors dimensions and type:</span></div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">const</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#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>(width, height, batch);</div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  input.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a531ec877bfc923dea3ab6f1be5e6e1ac">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3014ce2f4215e8a44331aa5daf3ba0d4">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, 1, DataType::F32));</div>
+<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a531ec877bfc923dea3ab6f1be5e6e1ac">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3014ce2f4215e8a44331aa5daf3ba0d4">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, 1, DataType::F32));</div>
+<div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div>
+<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="comment">// Configure softmax:</span></div>
+<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  softmax.<a class="code" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#a9daf8026e68559806afe7d0aa12693d6">configure</a>(&input, &output);</div>
+<div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div>
+<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="comment">// Allocate the input / output tensors:</span></div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  input.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a531ec877bfc923dea3ab6f1be5e6e1ac">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div>
+<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a531ec877bfc923dea3ab6f1be5e6e1ac">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div>
+<div class="line"><a name="l00074"></a><span class="lineno"> 74</span> </div>
+<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="comment">// Fill the input tensor:</span></div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="comment">// Simplest way: create an iterator to iterate through each element of the input tensor:</span></div>
+<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> input_window;</div>
+<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">use_tensor_dimensions</a>(input.<a class="code" href="classarm__compute_1_1_tensor.xhtml#aa68535e0983cc60a425253a72b162661">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>());</div>
+<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  std::cout << <span class="stringliteral">" Dimensions of the input's iterator:\n"</span>;</div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  std::cout << <span class="stringliteral">" X = [start="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">start</a>() << <span class="stringliteral">", end="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">end</a>() << <span class="stringliteral">", step="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">step</a>() << <span class="stringliteral">"]\n"</span>;</div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  std::cout << <span class="stringliteral">" Y = [start="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">start</a>() << <span class="stringliteral">", end="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">end</a>() << <span class="stringliteral">", step="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">step</a>() << <span class="stringliteral">"]\n"</span>;</div>
+<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  std::cout << <span class="stringliteral">" Z = [start="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">z</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">start</a>() << <span class="stringliteral">", end="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">z</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">end</a>() << <span class="stringliteral">", step="</span> << input_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">z</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">step</a>() << <span class="stringliteral">"]\n"</span>;</div>
+<div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div>
+<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="comment">// Create an iterator:</span></div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> input_it(&input, input_window);</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>  <span class="comment">// Iterate through the elements of src_data and copy them one by one to the input tensor:</span></div>
+<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="comment">// This is equivalent to:</span></div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="comment">// for( unsigned int z = 0; z < batch; ++z)</span></div>
+<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="comment">// {</span></div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="comment">// for( unsigned int y = 0; y < height; ++y)</span></div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="comment">// {</span></div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="comment">// for( unsigned int x = 0; x < width; ++x)</span></div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="comment">// {</span></div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="comment">// *reinterpret_cast<float*>( input.buffer() + input.info()->offset_element_in_bytes(Coordinates(x,y,z))) = src_data[ z * (width*height) + y * width + x];</span></div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="comment">// }</span></div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="comment">// }</span></div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="comment">// }</span></div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="comment">// Except it works for an arbitrary number of dimensions</span></div>
+<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(input_window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  {</div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  std::cout << <span class="stringliteral">"Setting item ["</span> << <span class="keywordtype">id</span>.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>() << <span class="stringliteral">","</span> << <span class="keywordtype">id</span>.y() << <span class="stringliteral">","</span> << <span class="keywordtype">id</span>.z() << <span class="stringliteral">"]\n"</span>;</div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  *<span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span> *<span class="keyword">></span>(input_it.ptr()) = src_data[<span class="keywordtype">id</span>.z() * (width * height) + <span class="keywordtype">id</span>.y() * width + <span class="keywordtype">id</span>.x()];</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>  input_it);</div>
+<div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="comment">// Run NEON softmax:</span></div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  softmax.<a class="code" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</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="comment">// More efficient way: create an iterator to iterate through each row (instead of each element) of the output tensor:</span></div>
+<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> output_window;</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">use_tensor_dimensions</a>(output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#aa68535e0983cc60a425253a72b162661">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(), <span class="comment">/* first_dimension =*/</span>Window::DimY); <span class="comment">// Iterate through the rows (not each element)</span></div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  std::cout << <span class="stringliteral">" Dimensions of the output's iterator:\n"</span>;</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  std::cout << <span class="stringliteral">" X = [start="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">start</a>() << <span class="stringliteral">", end="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">end</a>() << <span class="stringliteral">", step="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">step</a>() << <span class="stringliteral">"]\n"</span>;</div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  std::cout << <span class="stringliteral">" Y = [start="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">start</a>() << <span class="stringliteral">", end="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">end</a>() << <span class="stringliteral">", step="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">step</a>() << <span class="stringliteral">"]\n"</span>;</div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  std::cout << <span class="stringliteral">" Z = [start="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">z</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">start</a>() << <span class="stringliteral">", end="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">z</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">end</a>() << <span class="stringliteral">", step="</span> << output_window.<a class="code" href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">z</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">step</a>() << <span class="stringliteral">"]\n"</span>;</div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="comment">// Create an iterator:</span></div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> output_it(&output, output_window);</div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="comment">// Iterate through the rows of the output tensor and copy them to dst_data:</span></div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="comment">// This is equivalent to:</span></div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="comment">// for( unsigned int z = 0; z < batch; ++z)</span></div>
+<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="comment">// {</span></div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="comment">// for( unsigned int y = 0; y < height; ++y)</span></div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="comment">// {</span></div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="comment">// memcpy( dst_data + z * (width*height) + y * width, input.buffer() + input.info()->offset_element_in_bytes(Coordinates(0,y,z)), width * sizeof(float));</span></div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="comment">// }</span></div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// }</span></div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="comment">// Except it works for an arbitrary number of dimensions</span></div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(output_window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  std::cout << <span class="stringliteral">"Copying one row starting from ["</span> << <span class="keywordtype">id</span>.<a class="code" href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">x</a>() << <span class="stringliteral">","</span> << <span class="keywordtype">id</span>.y() << <span class="stringliteral">","</span> << <span class="keywordtype">id</span>.z() << <span class="stringliteral">"]\n"</span>;</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">// Copy one whole row:</span></div>
+<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  memcpy(dst_data + <span class="keywordtype">id</span>.z() * (width * height) + <span class="keywordtype">id</span>.y() * width, output_it.ptr(), width * <span class="keyword">sizeof</span>(float));</div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  },</div>
+<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  output_it);</div>
+<div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div>
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">delete</span>[] src_data;</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keyword">delete</span>[] dst_data;</div>
+<div class="line"><a name="l00142"></a><span class="lineno"> 142</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_min_max_location_8cpp_source.xhtml#l00089">MinMaxLocation.cpp:89</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00038">TensorShape.h:38</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a4103adbb45806b2f2002d44b91d0d206"><div class="ttname"><a href="_error_8h.xhtml#a4103adbb45806b2f2002d44b91d0d206">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(var)</div><div class="ttdoc">To avoid unused variables warnings. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00049">Error.h:49</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_1_1_dimension_xhtml_a27c3790df96e9523b0370e7e10c0d375"><div class="ttname"><a href="classarm__compute_1_1_window_1_1_dimension.xhtml#a27c3790df96e9523b0370e7e10c0d375">arm_compute::Window::Dimension::start</a></div><div class="ttdeci">constexpr int start() const </div><div class="ttdoc">Return the start of the dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00085">Window.h:85</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_n_e_softmax_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1_n_e_softmax_layer.xhtml">arm_compute::NESoftmaxLayer</a></div><div class="ttdoc">Basic function to compute a SoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_n_e_softmax_layer_8h_source.xhtml#l00046">NESoftmaxLayer.h:46</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_n_e_softmax_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1_n_e_softmax_layer.xhtml">arm_compute::NESoftmaxLayer</a></div><div class="ttdoc">Basic function to compute a SoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_n_e_softmax_layer_8h_source.xhtml#l00047">NESoftmaxLayer.h:47</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_window_xhtml_a14470b4cb59140a1b6ff3b8f16c89ab6"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">arm_compute::Window::use_tensor_dimensions</a></div><div class="ttdeci">void use_tensor_dimensions(const TensorShape &shape, size_t first_dimension=Window::DimX)</div><div class="ttdoc">Use the tensor's dimensions to fill the window dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00204">Window.inl:204</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_1_1_dimension_xhtml_aa9a8509af319b9e47f00c8fba23d368b"><div class="ttname"><a href="classarm__compute_1_1_window_1_1_dimension.xhtml#aa9a8509af319b9e47f00c8fba23d368b">arm_compute::Window::Dimension::end</a></div><div class="ttdeci">constexpr int end() const </div><div class="ttdoc">Return the end of the dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00090">Window.h:90</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_tensor_xhtml_a531ec877bfc923dea3ab6f1be5e6e1ac"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#a531ec877bfc923dea3ab6f1be5e6e1ac">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor&#39;s allocator. </div></div>
+<div class="ttc" id="classarm__compute_1_1_tensor_xhtml_a531ec877bfc923dea3ab6f1be5e6e1ac"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#a531ec877bfc923dea3ab6f1be5e6e1ac">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator. </div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml_aedd49c804f269c0e2c251c4efd44a275"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aedd49c804f269c0e2c251c4efd44a275">arm_compute::Window::y</a></div><div class="ttdeci">constexpr const Dimension & y() const </div><div class="ttdoc">Alias to access the second dimension of the window. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00137">Window.h:137</a></div></div>
-<div class="ttc" id="namespacearm__compute_xhtml_a6c0dcc38187027dcb89cd9724bc5a823"><div class="ttname"><a href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">arm_compute::execute_window_loop</a></div><div class="ttdeci">void execute_window_loop(const Window &w, L &&lambda_function, Ts &&...iterators)</div><div class="ttdoc">Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00176">Helpers.inl:176</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_xhtml_a6c0dcc38187027dcb89cd9724bc5a823"><div class="ttname"><a href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">arm_compute::execute_window_loop</a></div><div class="ttdeci">void execute_window_loop(const Window &w, L &&lambda_function, Ts &&...iterators)</div><div class="ttdoc">Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00127">Helpers.inl:127</a></div></div>
<div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item. </div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory. </div></div>
-<div class="ttc" id="classarm__compute_1_1_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml">arm_compute::Tensor</a></div><div class="ttdoc">Basic implementation of the tensor interface. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2runtime_2_tensor_8h_source.xhtml#l00037">Tensor.h:37</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml">arm_compute::Tensor</a></div><div class="ttdoc">Basic implementation of the tensor interface. </div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8h_source.xhtml#l00037">Tensor.h:37</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_softmax_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_softmax_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NESoftmaxLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function. </div></div>
<div class="ttc" id="classarm__compute_1_1_window_1_1_dimension_xhtml_a992b375fc3254afe2e38f63bc29a43d4"><div class="ttname"><a href="classarm__compute_1_1_window_1_1_dimension.xhtml#a992b375fc3254afe2e38f63bc29a43d4">arm_compute::Window::Dimension::step</a></div><div class="ttdeci">constexpr int step() const </div><div class="ttdoc">Return the step of the dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00095">Window.h:95</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a3014ce2f4215e8a44331aa5daf3ba0d4"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a3014ce2f4215e8a44331aa5daf3ba0d4">arm_compute::TensorAllocator::init</a></div><div class="ttdeci">void init(const TensorAllocator &allocator, const Coordinates &coords, TensorInfo sub_info)</div><div class="ttdoc">Shares the same backing memory with another tensor allocator, while the tensor info might be differen...</div></div>
-<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor&#39;s metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00042">TensorInfo.h:42</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00042">TensorInfo.h:42</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_softmax_layer_xhtml_a9daf8026e68559806afe7d0aa12693d6"><div class="ttname"><a href="classarm__compute_1_1_n_e_softmax_layer.xhtml#a9daf8026e68559806afe7d0aa12693d6">arm_compute::NESoftmaxLayer::configure</a></div><div class="ttdeci">void configure(ITensor *input, ITensor *output)</div><div class="ttdoc">Set the input and output tensors. </div></div>
-<div class="ttc" id="classarm__compute_1_1_iterator_xhtml"><div class="ttname"><a href="classarm__compute_1_1_iterator.xhtml">arm_compute::Iterator</a></div><div class="ttdoc">Iterator updated by execute_window_loop for each window element. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00251">Helpers.h:251</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_iterator_xhtml"><div class="ttname"><a href="classarm__compute_1_1_iterator.xhtml">arm_compute::Iterator</a></div><div class="ttdoc">Iterator updated by execute_window_loop for each window element. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00259">Helpers.h:259</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml_ade6a04ff1f61b38e07ddb8ef741c996b"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ade6a04ff1f61b38e07ddb8ef741c996b">arm_compute::Window::z</a></div><div class="ttdeci">constexpr const Dimension & z() const </div><div class="ttdoc">Alias to access the third dimension of the window. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00146">Window.h:146</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml">arm_compute::Window</a></div><div class="ttdoc">Describe a multidimensional execution window. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00039">Window.h:39</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_tensor_xhtml_aa68535e0983cc60a425253a72b162661"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#aa68535e0983cc60a425253a72b162661">arm_compute::Tensor::info</a></div><div class="ttdeci">ITensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor&#39;s metadata. </div></div>
+<div class="ttc" id="classarm__compute_1_1_tensor_xhtml_aa68535e0983cc60a425253a72b162661"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#aa68535e0983cc60a425253a72b162661">arm_compute::Tensor::info</a></div><div class="ttdeci">ITensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata. </div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml_ade63ce331b49eb66d330aab444e57ca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ade63ce331b49eb66d330aab444e57ca9">arm_compute::Window::x</a></div><div class="ttdeci">constexpr const Dimension & x() const </div><div class="ttdoc">Alias to access the first dimension of the window. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00128">Window.h:128</a></div></div>
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- <li class="footer">Generated on Fri Jun 23 2017 15:44:34 for Compute Library by
+ <li class="navelem"><a class="el" href="dir_1253bad92dedae5edd993ead924afb7b.xhtml">examples</a></li><li class="navelem"><a class="el" href="neon__copy__objects_8cpp.xhtml">neon_copy_objects.cpp</a></li>
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- <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.11 </li>
+ <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.6 </li>
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