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<div class="title">ExecutionHelpers.cpp</div> </div>
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<a href="_execution_helpers_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment"> * Copyright (c) 2018-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_execution_helpers_8h.xhtml">arm_compute/graph/detail/ExecutionHelpers.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="graph_2_graph_8h.xhtml">arm_compute/graph/Graph.h</a>&quot;</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_graph_context_8h.xhtml">arm_compute/graph/GraphContext.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_graph_manager_8h.xhtml">arm_compute/graph/GraphManager.h</a>&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="graph_2_tensor_8h.xhtml">arm_compute/graph/Tensor.h</a>&quot;</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_backend_registry_8h.xhtml">arm_compute/graph/backends/BackendRegistry.h</a>&quot;</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;{</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;<span class="keyword">namespace </span>graph</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;{</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;<span class="keyword">namespace </span>detail</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;{</div><div class="line"><a name="l00038"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#aa1d326a80e16fe0d4034637672d3b122"> 38</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#aa1d326a80e16fe0d4034637672d3b122">validate_all_nodes</a>(<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> &amp;g)</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;{</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; <span class="keyword">auto</span> &amp;nodes = g.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#a8a979250f01a5edba059a02748b10ea3">nodes</a>();</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; <span class="comment">// Create tasks</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;node : nodes)</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; {</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <span class="keywordflow">if</span>(node != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; <a class="code" href="namespacearm__compute_1_1graph.xhtml#a31488d29805a596498c0234ae392d35d">Target</a> assigned_target = node-&gt;assigned_target();</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend.xhtml">backends::IDeviceBackend</a> &amp;backend = <a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_backend_registry.xhtml#a1f3feffc5bf8a42636b0e5d4f5160041">backends::BackendRegistry::get</a>().<a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_backend_registry.xhtml#a0e2e9eb7cf2545949ea4130d768bea93">get_backend</a>(assigned_target);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> status = backend.<a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend.xhtml#a2ad722cd593bc5300108c8837ce5c980">validate_node</a>(*node);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(!<span class="keywordtype">bool</span>(status), status.<a class="code" href="classarm__compute_1_1_status.xhtml#ac8ba59c07d56602367dae64035abb1ef">error_description</a>().c_str());</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; }</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; }</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160;}</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a68c7ebb89adc0c2b21449cd15f6e8eba"> 55</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a68c7ebb89adc0c2b21449cd15f6e8eba">configure_all_tensors</a>(<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> &amp;g)</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160;{</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; <span class="keyword">auto</span> &amp;tensors = g.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#aabacbdcb2b491b129b119273a813bb67">tensors</a>();</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;tensor : tensors)</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <span class="keywordflow">if</span>(tensor &amp;&amp; tensor-&gt;handle() == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; {</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <a class="code" href="namespacearm__compute_1_1graph.xhtml#a31488d29805a596498c0234ae392d35d">Target</a> target = tensor-&gt;desc().target;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend.xhtml">backends::IDeviceBackend</a> &amp;backend = <a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_backend_registry.xhtml#a1f3feffc5bf8a42636b0e5d4f5160041">backends::BackendRegistry::get</a>().<a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_backend_registry.xhtml#a0e2e9eb7cf2545949ea4130d768bea93">get_backend</a>(target);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; handle = backend.<a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend.xhtml#a0f46e4fef818b4114b3d09c799944046">create_tensor</a>(*tensor);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(!handle, <span class="stringliteral">&quot;Couldn&#39;t create backend handle!&quot;</span>);</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; tensor-&gt;set_handle(std::move(handle));</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; }</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; }</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;}</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160;</div><div class="line"><a name="l00072"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a4312e5b00714a9de04fc54d2a5dcc119"> 72</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a4312e5b00714a9de04fc54d2a5dcc119">allocate_all_input_tensors</a>(<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml">INode</a> &amp;node)</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160;{</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#abe0cb3e4411a1c289e3e40e3f9d79fec">num_inputs</a>(); ++i)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; {</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a> *tensor = node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(i);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; <span class="keywordflow">if</span>(tensor != <span class="keyword">nullptr</span> &amp;&amp; !tensor-&gt;<a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml#aa3dc08c2e62f20a8fbfbcc46c6b4cb68">bound_edges</a>().empty())</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; {</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(!tensor-&gt;<a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml#a45c1ef0023ce430d009ec79c97761544">handle</a>(), <span class="stringliteral">&quot;Tensor handle is not configured!&quot;</span>);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; tensor-&gt;<a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml#a45c1ef0023ce430d009ec79c97761544">handle</a>()-&gt;<a class="code" href="classarm__compute_1_1graph_1_1_i_tensor_handle.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; }</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; }</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160;}</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#aa39e0698b2de495c235129b12fa39b08"> 85</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#aa39e0698b2de495c235129b12fa39b08">allocate_all_output_tensors</a>(<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml">INode</a> &amp;node)</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160;{</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a75d7c8bdf0059bb235880f8ef8d190bc">num_outputs</a>(); ++i)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; {</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a> *tensor = node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a27369471d9b2ba47746e1e923f585b9f">output</a>(i);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; <span class="keywordflow">if</span>(tensor != <span class="keyword">nullptr</span> &amp;&amp; !tensor-&gt;<a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml#aa3dc08c2e62f20a8fbfbcc46c6b4cb68">bound_edges</a>().empty())</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; {</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(!tensor-&gt;<a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml#a45c1ef0023ce430d009ec79c97761544">handle</a>(), <span class="stringliteral">&quot;Tensor handle is not configured!&quot;</span>);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; tensor-&gt;<a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml#a45c1ef0023ce430d009ec79c97761544">handle</a>()-&gt;<a class="code" href="classarm__compute_1_1graph_1_1_i_tensor_handle.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; }</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; }</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160;}</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160;</div><div class="line"><a name="l00098"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a3ef7cb28de6aea7026a1e8572639f300"> 98</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a3ef7cb28de6aea7026a1e8572639f300">allocate_const_tensors</a>(<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> &amp;g)</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160;{</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;node : g.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#a8a979250f01a5edba059a02748b10ea3">nodes</a>())</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; <span class="keywordflow">if</span>(node != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; {</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <span class="keywordflow">switch</span>(node-&gt;type())</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; {</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#acac9cbaeea226ed297804c012dc12b16a79ceee9f8e3c1f0cc74223e05d2448bf">NodeType::Const</a>:</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#acac9cbaeea226ed297804c012dc12b16a324118a6721dd6b8a9b9f4e327df2bf5">NodeType::Input</a>:</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#aa39e0698b2de495c235129b12fa39b08">allocate_all_output_tensors</a>(*node);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#acac9cbaeea226ed297804c012dc12b16a29c2c02a361c9d7028472e5d92cd4a54">NodeType::Output</a>:</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a4312e5b00714a9de04fc54d2a5dcc119">allocate_all_input_tensors</a>(*node);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <span class="keywordflow">default</span>:</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; }</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; }</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; }</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160;}</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160;</div><div class="line"><a name="l00119"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a766bb5055ef388455c941869b32981ed"> 119</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a766bb5055ef388455c941869b32981ed">allocate_all_tensors</a>(<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> &amp;g)</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160;{</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; <span class="keyword">auto</span> &amp;tensors = g.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#aabacbdcb2b491b129b119273a813bb67">tensors</a>();</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;tensor : tensors)</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; {</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; <span class="keywordflow">if</span>(tensor &amp;&amp; !tensor-&gt;bound_edges().empty() &amp;&amp; tensor-&gt;handle() != <span class="keyword">nullptr</span> &amp;&amp; tensor-&gt;handle()-&gt;tensor().info()-&gt;is_resizable() &amp;&amp; tensor-&gt;handle()-&gt;tensor().is_used())</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; {</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; tensor-&gt;handle()-&gt;allocate();</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; }</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; }</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160;}</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160;</div><div class="line"><a name="l00132"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a82d07d3d612bee8ff703226ff9d5d452"> 132</a></span>&#160;<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml">ExecutionWorkload</a> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a82d07d3d612bee8ff703226ff9d5d452">configure_all_nodes</a>(<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> &amp;g, <a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml">GraphContext</a> &amp;ctx, <span class="keyword">const</span> std::vector&lt;NodeID&gt; &amp;node_order)</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160;{</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; <a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml">ExecutionWorkload</a> workload;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#a2360aba01fa93c8493ba96683c4fb89c">graph</a> = &amp;g;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#abda8fc9044eb180a388401a256d4ed05">ctx</a> = &amp;ctx;</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; <span class="comment">// Reserve memory for tasks</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#ae7d2dcab167d9eba3483a1fa1f330c7f">tasks</a>.reserve(node_order.size());</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; <span class="comment">// Create tasks</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;node_id : node_order)</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; {</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; <span class="keyword">auto</span> node = g.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#af8baf1f3da6d42a94d0569395ece882a">node</a>(node_id);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; <span class="keywordflow">if</span>(node != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; {</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; <a class="code" href="namespacearm__compute_1_1graph.xhtml#a31488d29805a596498c0234ae392d35d">Target</a> assigned_target = node-&gt;<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#aa26cd423e8fc9233bd2bff46f82f46ab">assigned_target</a>();</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend.xhtml">backends::IDeviceBackend</a> &amp;backend = <a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_backend_registry.xhtml#a1f3feffc5bf8a42636b0e5d4f5160041">backends::BackendRegistry::get</a>().<a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_backend_registry.xhtml#a0e2e9eb7cf2545949ea4130d768bea93">get_backend</a>(assigned_target);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; std::unique_ptr&lt;IFunction&gt; func = backend.<a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend.xhtml#af989fb4cdd6783b776e6b3feceb9fdcb">configure_node</a>(*node, ctx);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; <span class="keywordflow">if</span>(func != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; {</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#ae7d2dcab167d9eba3483a1fa1f330c7f">tasks</a>.emplace_back(<a class="code" href="structarm__compute_1_1graph_1_1_execution_task.xhtml">ExecutionTask</a>(std::move(func), node));</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; }</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; }</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; }</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; <span class="comment">// Add inputs and outputs</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;node : g.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#a8a979250f01a5edba059a02748b10ea3">nodes</a>())</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; {</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; <span class="keywordflow">if</span>(node != <span class="keyword">nullptr</span> &amp;&amp; node-&gt;type() == <a class="code" href="namespacearm__compute_1_1graph.xhtml#acac9cbaeea226ed297804c012dc12b16a324118a6721dd6b8a9b9f4e327df2bf5">NodeType::Input</a>)</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; {</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#aa8479921c38182de22a1a1a4d24481e6">inputs</a>.push_back(node-&gt;output(0));</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; }</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; <span class="keywordflow">if</span>(node != <span class="keyword">nullptr</span> &amp;&amp; node-&gt;type() == <a class="code" href="namespacearm__compute_1_1graph.xhtml#acac9cbaeea226ed297804c012dc12b16a29c2c02a361c9d7028472e5d92cd4a54">NodeType::Output</a>)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; {</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#af19f4e3beb0cb0a38b9b213d47c813c7">outputs</a>.push_back(node-&gt;input(0));</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; }</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; }</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160;}</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160;</div><div class="line"><a name="l00175"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a28280ac6b0db4c2385ac8c18a4ad8f3c"> 175</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a28280ac6b0db4c2385ac8c18a4ad8f3c">release_unused_tensors</a>(<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> &amp;g)</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160;{</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;tensor : g.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#aabacbdcb2b491b129b119273a813bb67">tensors</a>())</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; {</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; <span class="keywordflow">if</span>(tensor != <span class="keyword">nullptr</span> &amp;&amp; tensor-&gt;handle() != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; {</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; tensor-&gt;handle()-&gt;release_if_unused();</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; }</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; }</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160;}</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160;</div><div class="line"><a name="l00186"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#aa2dc91a4b4f5ad19935984344f1b67a1"> 186</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#aa2dc91a4b4f5ad19935984344f1b67a1">call_tensor_accessor</a>(<a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a> *tensor)</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160;{</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!tensor);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; tensor-&gt;<a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml#a88d65b04d950e6aba147624fcd308b34">call_accessor</a>();</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160;}</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160;</div><div class="line"><a name="l00192"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a6f2e226c8b1e478b0162c3d9327b032e"> 192</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a6f2e226c8b1e478b0162c3d9327b032e">call_all_const_node_accessors</a>(<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> &amp;g)</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160;{</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; <span class="keyword">auto</span> &amp;nodes = g.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#a8a979250f01a5edba059a02748b10ea3">nodes</a>();</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160;</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;node : nodes)</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; {</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; <span class="keywordflow">if</span>(node != <span class="keyword">nullptr</span> &amp;&amp; node-&gt;type() == <a class="code" href="namespacearm__compute_1_1graph.xhtml#acac9cbaeea226ed297804c012dc12b16a79ceee9f8e3c1f0cc74223e05d2448bf">NodeType::Const</a>)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#aa2dc91a4b4f5ad19935984344f1b67a1">call_tensor_accessor</a>(node-&gt;output(0));</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; }</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; }</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160;}</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160;</div><div class="line"><a name="l00205"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#adff00463fb88bf00a886e0ecef6c0b6a"> 205</a></span>&#160;<span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#adff00463fb88bf00a886e0ecef6c0b6a">call_all_input_node_accessors</a>(<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml">ExecutionWorkload</a> &amp;workload)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160;{</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; <span class="keywordtype">bool</span> is_valid = <span class="keyword">true</span>;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; <a class="code" href="namespacearm__compute_1_1utility.xhtml#a067ebd28103d827b6ec17032e2344064">std::for_each</a>(std::begin(workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#aa8479921c38182de22a1a1a4d24481e6">inputs</a>), std::end(workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#aa8479921c38182de22a1a1a4d24481e6">inputs</a>), [&amp;](<a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a> * input_tensor)</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; {</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; bool valid_input = (input_tensor != nullptr) &amp;&amp; input_tensor-&gt;call_accessor();</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; is_valid = is_valid &amp;&amp; valid_input;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; });</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; <span class="keywordflow">return</span> is_valid;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160;}</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160;</div><div class="line"><a name="l00216"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a17b5e700b317feb19fc3cefd23a1d8bb"> 216</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a17b5e700b317feb19fc3cefd23a1d8bb">prepare_all_tasks</a>(<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml">ExecutionWorkload</a> &amp;workload)</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160;{</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#a2360aba01fa93c8493ba96683c4fb89c">graph</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;task : workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#ae7d2dcab167d9eba3483a1fa1f330c7f">tasks</a>)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; {</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; task.prepare();</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a28280ac6b0db4c2385ac8c18a4ad8f3c">release_unused_tensors</a>(*workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#a2360aba01fa93c8493ba96683c4fb89c">graph</a>);</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; }</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160;}</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160;</div><div class="line"><a name="l00226"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a7641ee3c6374e97c597ce6ee05545b59"> 226</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#a7641ee3c6374e97c597ce6ee05545b59">call_all_tasks</a>(<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml">ExecutionWorkload</a> &amp;workload)</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160;{</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#abda8fc9044eb180a388401a256d4ed05">ctx</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; <span class="comment">// Acquire memory for the transition buffers</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;mm_ctx : workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#abda8fc9044eb180a388401a256d4ed05">ctx</a>-&gt;<a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml#a764b116846c97f7657a86c22583575ac">memory_managers</a>())</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; {</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; <span class="keywordflow">if</span>(mm_ctx.second.cross_group != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; {</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; mm_ctx.second.cross_group-&gt;acquire();</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; }</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160; }</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160;</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; <span class="comment">// Execute tasks</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;task : workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#ae7d2dcab167d9eba3483a1fa1f330c7f">tasks</a>)</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; {</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; task();</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; }</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160;</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; <span class="comment">// Release memory for the transition buffers</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160; <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;mm_ctx : workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#abda8fc9044eb180a388401a256d4ed05">ctx</a>-&gt;<a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml#a764b116846c97f7657a86c22583575ac">memory_managers</a>())</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; {</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; <span class="keywordflow">if</span>(mm_ctx.second.cross_group != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; {</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; mm_ctx.second.cross_group-&gt;release();</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; }</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; }</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160;}</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160;</div><div class="line"><a name="l00255"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1detail.xhtml#aaf7f4e1e2142802dd1c5a983ee156e77"> 255</a></span>&#160;<span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1graph_1_1detail.xhtml#aaf7f4e1e2142802dd1c5a983ee156e77">call_all_output_node_accessors</a>(<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml">ExecutionWorkload</a> &amp;workload)</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160;{</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; <span class="keywordtype">bool</span> is_valid = <span class="keyword">true</span>;</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; <a class="code" href="namespacearm__compute_1_1utility.xhtml#a067ebd28103d827b6ec17032e2344064">std::for_each</a>(std::begin(workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#af19f4e3beb0cb0a38b9b213d47c813c7">outputs</a>), std::end(workload.<a class="code" href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#af19f4e3beb0cb0a38b9b213d47c813c7">outputs</a>), [&amp;](<a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a> * output_tensor)</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; {</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160; bool valid_output = (output_tensor != nullptr) &amp;&amp; output_tensor-&gt;call_accessor();</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; is_valid = is_valid &amp;&amp; valid_output;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; });</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; <span class="keywordflow">return</span> is_valid;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160;}</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160;} <span class="comment">// namespace detail</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160;} <span class="comment">// namespace graph</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160;} <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="structarm__compute_1_1graph_1_1_execution_workload_xhtml_aa8479921c38182de22a1a1a4d24481e6"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#aa8479921c38182de22a1a1a4d24481e6">arm_compute::graph::ExecutionWorkload::inputs</a></div><div class="ttdeci">std::vector&lt; Tensor * &gt; inputs</div><div class="ttdoc">Input handles.</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8h_source.xhtml#l00100">Workload.h:100</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_tensor_xhtml_a88d65b04d950e6aba147624fcd308b34"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml#a88d65b04d950e6aba147624fcd308b34">arm_compute::graph::Tensor::call_accessor</a></div><div class="ttdeci">bool call_accessor()</div><div class="ttdoc">Calls accessor on tensor.</div><div class="ttdef"><b>Definition:</b> <a href="graph_2_tensor_8cpp_source.xhtml#l00075">Tensor.cpp:75</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend_xhtml_af989fb4cdd6783b776e6b3feceb9fdcb"><div class="ttname"><a href="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend.xhtml#af989fb4cdd6783b776e6b3feceb9fdcb">arm_compute::graph::backends::IDeviceBackend::configure_node</a></div><div class="ttdeci">virtual std::unique_ptr&lt; arm_compute::IFunction &gt; configure_node(INode &amp;node, GraphContext &amp;ctx)=0</div><div class="ttdoc">Configure a backend Node.</div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_context_xhtml_a764b116846c97f7657a86c22583575ac"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph_context.xhtml#a764b116846c97f7657a86c22583575ac">arm_compute::graph::GraphContext::memory_managers</a></div><div class="ttdeci">std::map&lt; Target, MemoryManagerContext &gt; &amp; memory_managers()</div><div class="ttdoc">Gets the memory managers map.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_context_8cpp_source.xhtml#l00072">GraphContext.cpp:72</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_tensor_xhtml_a45c1ef0023ce430d009ec79c97761544"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml#a45c1ef0023ce430d009ec79c97761544">arm_compute::graph::Tensor::handle</a></div><div class="ttdeci">ITensorHandle * handle()</div><div class="ttdoc">Backend tensor handle accessor.</div><div class="ttdef"><b>Definition:</b> <a href="graph_2_tensor_8cpp_source.xhtml#l00055">Tensor.cpp:55</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_aa2dc91a4b4f5ad19935984344f1b67a1"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#aa2dc91a4b4f5ad19935984344f1b67a1">arm_compute::graph::detail::call_tensor_accessor</a></div><div class="ttdeci">void call_tensor_accessor(Tensor *tensor)</div><div class="ttdoc">Calls accessor of a given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00186">ExecutionHelpers.cpp:186</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_aa26cd423e8fc9233bd2bff46f82f46ab"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#aa26cd423e8fc9233bd2bff46f82f46ab">arm_compute::graph::INode::assigned_target</a></div><div class="ttdeci">Target assigned_target() const</div><div class="ttdoc">Returns assigned target for this node.</div><div class="ttdef"><b>Definition:</b> <a href="_i_node_8cpp_source.xhtml#l00198">INode.cpp:198</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_a75d7c8bdf0059bb235880f8ef8d190bc"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#a75d7c8bdf0059bb235880f8ef8d190bc">arm_compute::graph::INode::num_outputs</a></div><div class="ttdeci">size_t num_outputs() const</div><div class="ttdoc">Returns number of outputs of the node.</div><div class="ttdef"><b>Definition:</b> <a href="_i_node_8cpp_source.xhtml#l00183">INode.cpp:183</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_execution_workload_xhtml"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_execution_workload.xhtml">arm_compute::graph::ExecutionWorkload</a></div><div class="ttdoc">Execution workload.</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8h_source.xhtml#l00098">Workload.h:98</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_a766bb5055ef388455c941869b32981ed"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#a766bb5055ef388455c941869b32981ed">arm_compute::graph::detail::allocate_all_tensors</a></div><div class="ttdeci">void allocate_all_tensors(Graph &amp;g)</div><div class="ttdoc">Allocates all tensors of a graph.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00119">ExecutionHelpers.cpp:119</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1backends_1_1_backend_registry_xhtml_a0e2e9eb7cf2545949ea4130d768bea93"><div class="ttname"><a href="classarm__compute_1_1graph_1_1backends_1_1_backend_registry.xhtml#a0e2e9eb7cf2545949ea4130d768bea93">arm_compute::graph::backends::BackendRegistry::get_backend</a></div><div class="ttdeci">IDeviceBackend &amp; get_backend(Target target)</div><div class="ttdoc">Get a backend from the registry.</div><div class="ttdef"><b>Definition:</b> <a href="_backend_registry_8cpp_source.xhtml#l00051">BackendRegistry.cpp:51</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div>
<div class="ttc" id="classarm__compute_1_1_status_xhtml"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml">arm_compute::Status</a></div><div class="ttdoc">Status class.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00052">Error.h:52</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_a17b5e700b317feb19fc3cefd23a1d8bb"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#a17b5e700b317feb19fc3cefd23a1d8bb">arm_compute::graph::detail::prepare_all_tasks</a></div><div class="ttdeci">void prepare_all_tasks(ExecutionWorkload &amp;workload)</div><div class="ttdoc">Prepares all tasks for execution.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00216">ExecutionHelpers.cpp:216</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_context_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph_context.xhtml">arm_compute::graph::GraphContext</a></div><div class="ttdoc">Graph context.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_context_8h_source.xhtml#l00049">GraphContext.h:49</a></div></div>
<div class="ttc" id="_execution_helpers_8h_xhtml"><div class="ttname"><a href="_execution_helpers_8h.xhtml">ExecutionHelpers.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2018 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend_xhtml_a0f46e4fef818b4114b3d09c799944046"><div class="ttname"><a href="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend.xhtml#a0f46e4fef818b4114b3d09c799944046">arm_compute::graph::backends::IDeviceBackend::create_tensor</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; create_tensor(const Tensor &amp;tensor)=0</div><div class="ttdoc">Create a backend Tensor.</div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_a68c7ebb89adc0c2b21449cd15f6e8eba"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#a68c7ebb89adc0c2b21449cd15f6e8eba">arm_compute::graph::detail::configure_all_tensors</a></div><div class="ttdeci">void configure_all_tensors(Graph &amp;g)</div><div class="ttdoc">Configures all nodes of a graph.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00055">ExecutionHelpers.cpp:55</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_xhtml_aabacbdcb2b491b129b119273a813bb67"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph.xhtml#aabacbdcb2b491b129b119273a813bb67">arm_compute::graph::Graph::tensors</a></div><div class="ttdeci">std::vector&lt; std::unique_ptr&lt; Tensor &gt; &gt; &amp; tensors()</div><div class="ttdoc">Returns tensors of graph.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8cpp_source.xhtml#l00194">Graph.cpp:194</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml">arm_compute::graph::INode</a></div><div class="ttdoc">Node interface.</div><div class="ttdef"><b>Definition:</b> <a href="_i_node_8h_source.xhtml#l00045">INode.h:45</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_a4312e5b00714a9de04fc54d2a5dcc119"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#a4312e5b00714a9de04fc54d2a5dcc119">arm_compute::graph::detail::allocate_all_input_tensors</a></div><div class="ttdeci">void allocate_all_input_tensors(INode &amp;node)</div><div class="ttdoc">Allocates all input tensors of a node.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00072">ExecutionHelpers.cpp:72</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_aa1d326a80e16fe0d4034637672d3b122"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#aa1d326a80e16fe0d4034637672d3b122">arm_compute::graph::detail::validate_all_nodes</a></div><div class="ttdeci">void validate_all_nodes(Graph &amp;g)</div><div class="ttdoc">Validates all nodes.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00038">ExecutionHelpers.cpp:38</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_execution_workload_xhtml_ae7d2dcab167d9eba3483a1fa1f330c7f"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#ae7d2dcab167d9eba3483a1fa1f330c7f">arm_compute::graph::ExecutionWorkload::tasks</a></div><div class="ttdeci">std::vector&lt; ExecutionTask &gt; tasks</div><div class="ttdoc">Execution workload.</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8h_source.xhtml#l00102">Workload.h:102</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_aa39e0698b2de495c235129b12fa39b08"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#aa39e0698b2de495c235129b12fa39b08">arm_compute::graph::detail::allocate_all_output_tensors</a></div><div class="ttdeci">void allocate_all_output_tensors(INode &amp;node)</div><div class="ttdoc">Allocates all output tensors of a node.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00085">ExecutionHelpers.cpp:85</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_a27369471d9b2ba47746e1e923f585b9f"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#a27369471d9b2ba47746e1e923f585b9f">arm_compute::graph::INode::output</a></div><div class="ttdeci">Tensor * output(size_t idx) const</div><div class="ttdoc">Returns the tensor of a given output of the node.</div><div class="ttdef"><b>Definition:</b> <a href="_i_node_8cpp_source.xhtml#l00158">INode.cpp:158</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_a28280ac6b0db4c2385ac8c18a4ad8f3c"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#a28280ac6b0db4c2385ac8c18a4ad8f3c">arm_compute::graph::detail::release_unused_tensors</a></div><div class="ttdeci">void release_unused_tensors(Graph &amp;g)</div><div class="ttdoc">Release the memory of all unused const nodes.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00175">ExecutionHelpers.cpp:175</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_i_tensor_handle_xhtml_aa8a4946cd749d482dd996874d295af85"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_tensor_handle.xhtml#aa8a4946cd749d482dd996874d295af85">arm_compute::graph::ITensorHandle::allocate</a></div><div class="ttdeci">virtual void allocate()=0</div><div class="ttdoc">Allocates backend memory for the handle.</div></div>
<div class="ttc" id="_graph_manager_8h_xhtml"><div class="ttname"><a href="_graph_manager_8h.xhtml">GraphManager.h</a></div></div>
<div class="ttc" id="graph_2_tensor_8h_xhtml"><div class="ttname"><a href="graph_2_tensor_8h.xhtml">Tensor.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_a6f2e226c8b1e478b0162c3d9327b032e"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#a6f2e226c8b1e478b0162c3d9327b032e">arm_compute::graph::detail::call_all_const_node_accessors</a></div><div class="ttdeci">void call_all_const_node_accessors(Graph &amp;g)</div><div class="ttdoc">Call all const node accessors.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00192">ExecutionHelpers.cpp:192</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_adff00463fb88bf00a886e0ecef6c0b6a"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#adff00463fb88bf00a886e0ecef6c0b6a">arm_compute::graph::detail::call_all_input_node_accessors</a></div><div class="ttdeci">bool call_all_input_node_accessors(ExecutionWorkload &amp;workload)</div><div class="ttdoc">Call all input node accessors.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00205">ExecutionHelpers.cpp:205</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1backends_1_1_backend_registry_xhtml_a1f3feffc5bf8a42636b0e5d4f5160041"><div class="ttname"><a href="classarm__compute_1_1graph_1_1backends_1_1_backend_registry.xhtml#a1f3feffc5bf8a42636b0e5d4f5160041">arm_compute::graph::backends::BackendRegistry::get</a></div><div class="ttdeci">static BackendRegistry &amp; get()</div><div class="ttdoc">Gets backend registry instance.</div><div class="ttdef"><b>Definition:</b> <a href="_backend_registry_8cpp_source.xhtml#l00039">BackendRegistry.cpp:39</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a31488d29805a596498c0234ae392d35d"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a31488d29805a596498c0234ae392d35d">arm_compute::graph::Target</a></div><div class="ttdeci">Target</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00089">Types.h:89</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1utility_xhtml_a067ebd28103d827b6ec17032e2344064"><div class="ttname"><a href="namespacearm__compute_1_1utility.xhtml#a067ebd28103d827b6ec17032e2344064">arm_compute::utility::for_each</a></div><div class="ttdeci">void for_each(F &amp;&amp;)</div><div class="ttdoc">Base case of for_each.</div><div class="ttdef"><b>Definition:</b> <a href="_utility_8h_source.xhtml#l00093">Utility.h:93</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_execution_workload_xhtml_a2360aba01fa93c8493ba96683c4fb89c"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#a2360aba01fa93c8493ba96683c4fb89c">arm_compute::graph::ExecutionWorkload::graph</a></div><div class="ttdeci">Graph * graph</div><div class="ttdoc">Graph bound to the workload.</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8h_source.xhtml#l00103">Workload.h:103</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend_xhtml_a2ad722cd593bc5300108c8837ce5c980"><div class="ttname"><a href="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend.xhtml#a2ad722cd593bc5300108c8837ce5c980">arm_compute::graph::backends::IDeviceBackend::validate_node</a></div><div class="ttdeci">virtual Status validate_node(INode &amp;node)=0</div><div class="ttdoc">Validate a node.</div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph.xhtml">arm_compute::graph::Graph</a></div><div class="ttdoc">Graph class.</div><div class="ttdef"><b>Definition:</b> <a href="graph_2_graph_8h_source.xhtml#l00050">Graph.h:50</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_a82d07d3d612bee8ff703226ff9d5d452"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#a82d07d3d612bee8ff703226ff9d5d452">arm_compute::graph::detail::configure_all_nodes</a></div><div class="ttdeci">ExecutionWorkload configure_all_nodes(Graph &amp;g, GraphContext &amp;ctx, const std::vector&lt; NodeID &gt; &amp;node_order)</div><div class="ttdoc">Configures all nodes of graph.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00132">ExecutionHelpers.cpp:132</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_acac9cbaeea226ed297804c012dc12b16a79ceee9f8e3c1f0cc74223e05d2448bf"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#acac9cbaeea226ed297804c012dc12b16a79ceee9f8e3c1f0cc74223e05d2448bf">arm_compute::graph::NodeType::Const</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_xhtml_a8a979250f01a5edba059a02748b10ea3"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph.xhtml#a8a979250f01a5edba059a02748b10ea3">arm_compute::graph::Graph::nodes</a></div><div class="ttdeci">const std::vector&lt; NodeID &gt; &amp; nodes(NodeType type)</div><div class="ttdoc">Returns graph input nodes.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8cpp_source.xhtml#l00174">Graph.cpp:174</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_a3ef7cb28de6aea7026a1e8572639f300"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#a3ef7cb28de6aea7026a1e8572639f300">arm_compute::graph::detail::allocate_const_tensors</a></div><div class="ttdeci">void allocate_const_tensors(Graph &amp;g)</div><div class="ttdoc">Allocates const tensor of a given graph.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00098">ExecutionHelpers.cpp:98</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_xhtml_af8baf1f3da6d42a94d0569395ece882a"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph.xhtml#af8baf1f3da6d42a94d0569395ece882a">arm_compute::graph::Graph::node</a></div><div class="ttdeci">const INode * node(NodeID id) const</div><div class="ttdoc">Get node object given its id.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8cpp_source.xhtml#l00204">Graph.cpp:204</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_execution_workload_xhtml_abda8fc9044eb180a388401a256d4ed05"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#abda8fc9044eb180a388401a256d4ed05">arm_compute::graph::ExecutionWorkload::ctx</a></div><div class="ttdeci">GraphContext * ctx</div><div class="ttdoc">Graph execution context.</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8h_source.xhtml#l00104">Workload.h:104</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_acac9cbaeea226ed297804c012dc12b16a324118a6721dd6b8a9b9f4e327df2bf5"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#acac9cbaeea226ed297804c012dc12b16a324118a6721dd6b8a9b9f4e327df2bf5">arm_compute::graph::NodeType::Input</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_abe0cb3e4411a1c289e3e40e3f9d79fec"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#abe0cb3e4411a1c289e3e40e3f9d79fec">arm_compute::graph::INode::num_inputs</a></div><div class="ttdeci">size_t num_inputs() const</div><div class="ttdoc">Returns number of inputs of the node.</div><div class="ttdef"><b>Definition:</b> <a href="_i_node_8cpp_source.xhtml#l00178">INode.cpp:178</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_tensor_xhtml_aa3dc08c2e62f20a8fbfbcc46c6b4cb68"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml#aa3dc08c2e62f20a8fbfbcc46c6b4cb68">arm_compute::graph::Tensor::bound_edges</a></div><div class="ttdeci">std::set&lt; EdgeID &gt; bound_edges() const</div><div class="ttdoc">Accessor the edges that are bound with the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="graph_2_tensor_8cpp_source.xhtml#l00111">Tensor.cpp:111</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1backends_1_1_i_device_backend.xhtml">arm_compute::graph::backends::IDeviceBackend</a></div><div class="ttdoc">Device backend interface.</div><div class="ttdef"><b>Definition:</b> <a href="_i_device_backend_8h_source.xhtml#l00047">IDeviceBackend.h:47</a></div></div>
<div class="ttc" id="graph_2_graph_8h_xhtml"><div class="ttname"><a href="graph_2_graph_8h.xhtml">Graph.h</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_execution_task_xhtml"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_execution_task.xhtml">arm_compute::graph::ExecutionTask</a></div><div class="ttdoc">Execution task.</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8h_source.xhtml#l00070">Workload.h:70</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_a7641ee3c6374e97c597ce6ee05545b59"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#a7641ee3c6374e97c597ce6ee05545b59">arm_compute::graph::detail::call_all_tasks</a></div><div class="ttdeci">void call_all_tasks(ExecutionWorkload &amp;workload)</div><div class="ttdoc">Executes all tasks of a workload.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00226">ExecutionHelpers.cpp:226</a></div></div>
<div class="ttc" id="_graph_context_8h_xhtml"><div class="ttname"><a href="_graph_context_8h.xhtml">GraphContext.h</a></div></div>
<div class="ttc" id="_backend_registry_8h_xhtml"><div class="ttname"><a href="_backend_registry_8h.xhtml">BackendRegistry.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_ad7c09b0faaf3c808b0489012204852a9"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">arm_compute::graph::INode::input</a></div><div class="ttdeci">Tensor * input(size_t idx) const</div><div class="ttdoc">Returns the tensor of a given input of the node.</div><div class="ttdef"><b>Definition:</b> <a href="_i_node_8cpp_source.xhtml#l00150">INode.cpp:150</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1detail_xhtml_aaf7f4e1e2142802dd1c5a983ee156e77"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1detail.xhtml#aaf7f4e1e2142802dd1c5a983ee156e77">arm_compute::graph::detail::call_all_output_node_accessors</a></div><div class="ttdeci">bool call_all_output_node_accessors(ExecutionWorkload &amp;workload)</div><div class="ttdoc">Call all output node accessors.</div><div class="ttdef"><b>Definition:</b> <a href="_execution_helpers_8cpp_source.xhtml#l00255">ExecutionHelpers.cpp:255</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_execution_workload_xhtml_af19f4e3beb0cb0a38b9b213d47c813c7"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_execution_workload.xhtml#af19f4e3beb0cb0a38b9b213d47c813c7">arm_compute::graph::ExecutionWorkload::outputs</a></div><div class="ttdeci">std::vector&lt; Tensor * &gt; outputs</div><div class="ttdoc">Output handles.</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8h_source.xhtml#l00101">Workload.h:101</a></div></div>
<div class="ttc" id="classarm__compute_1_1_status_xhtml_ac8ba59c07d56602367dae64035abb1ef"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml#ac8ba59c07d56602367dae64035abb1ef">arm_compute::Status::error_description</a></div><div class="ttdeci">std::string error_description() const</div><div class="ttdoc">Gets error description if any.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00097">Error.h:97</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml">arm_compute::graph::Tensor</a></div><div class="ttdoc">Tensor object.</div><div class="ttdef"><b>Definition:</b> <a href="graph_2_tensor_8h_source.xhtml#l00041">Tensor.h:41</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_acac9cbaeea226ed297804c012dc12b16a29c2c02a361c9d7028472e5d92cd4a54"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#acac9cbaeea226ed297804c012dc12b16a29c2c02a361c9d7028472e5d92cd4a54">arm_compute::graph::NodeType::Output</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div>
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