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<div class="title">NeonTensorHandleTests.cpp</div> </div>
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<a href="_neon_tensor_handle_tests_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> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#include <<a class="code" href="_graph_8hpp.xhtml">Graph.hpp</a>></span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include <<a class="code" href="_network_8hpp.xhtml">Network.hpp</a>></span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_neon_tensor_handle_8hpp.xhtml">neon/NeonTensorHandle.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_neon_tensor_handle_factory_8hpp.xhtml">neon/NeonTensorHandleFactory.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_numeric_cast_8hpp.xhtml">armnn/utility/NumericCast.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_polymorphic_downcast_8hpp.xhtml">armnn/utility/PolymorphicDowncast.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_graph_utils_8hpp.xhtml">test/GraphUtils.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <arm_compute/runtime/Allocator.h></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <<a class="code" href="_common_test_utils_8hpp.xhtml">backendsCommon/test/CommonTestUtils.hpp</a>></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <boost/test/unit_test.hpp></span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include <<a class="code" href="_assert_8hpp.xhtml">armnn/utility/Assert.hpp</a>></span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <a class="code" href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(NeonTensorHandleTests)</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00024"></a><span class="lineno"><a class="line" href="_neon_tensor_handle_tests_8cpp.xhtml#aac841c1bae3769284710cd5bfc633884"> 24</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(NeonTensorHandleGetCapabilitiesNoPadding)</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> {</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  std::shared_ptr<NeonMemoryManager> memoryManager = std::make_shared<NeonMemoryManager>();</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <a class="code" href="classarmnn_1_1_neon_tensor_handle_factory.xhtml">NeonTensorHandleFactory</a> handleFactory(memoryManager);</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = network->AddInputLayer(0);</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">SoftmaxDescriptor</a> descriptor;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  descriptor.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = 1.0f;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* softmax = network->AddSoftmaxLayer(descriptor);</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(2);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(softmax-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  softmax-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="comment">// No padding required for input</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  std::vector<Capability> capabilities = handleFactory.GetCapabilities(input,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  softmax,</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <a class="code" href="namespacearmnn.xhtml#a10c50bc964cc8cc559eebcd7df5a8af3aa47abd1077ef632a38ada05b6edbf389">CapabilityClass::PaddingRequired</a>);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  BOOST_TEST(capabilities.empty());</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">// No padding required for Softmax</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  capabilities = handleFactory.GetCapabilities(softmax, output, <a class="code" href="namespacearmnn.xhtml#a10c50bc964cc8cc559eebcd7df5a8af3aa47abd1077ef632a38ada05b6edbf389">CapabilityClass::PaddingRequired</a>);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  BOOST_TEST(capabilities.empty());</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="comment">// No padding required for output</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  capabilities = handleFactory.GetCapabilities(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearmnn.xhtml#a10c50bc964cc8cc559eebcd7df5a8af3aa47abd1077ef632a38ada05b6edbf389">CapabilityClass::PaddingRequired</a>);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  BOOST_TEST(capabilities.empty());</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> }</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"><a class="line" href="_neon_tensor_handle_tests_8cpp.xhtml#a2426881522a7e2dbf60553c9a7f42054"> 57</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(NeonTensorHandleGetCapabilitiesPadding)</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>  std::shared_ptr<NeonMemoryManager> memoryManager = std::make_shared<NeonMemoryManager>();</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="classarmnn_1_1_neon_tensor_handle_factory.xhtml">NeonTensorHandleFactory</a> handleFactory(memoryManager);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = network->AddInputLayer(0);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling = network->AddPooling2dLayer(descriptor);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(2);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="comment">// No padding required for input</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  std::vector<Capability> capabilities = handleFactory.GetCapabilities(input,</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  pooling,</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="namespacearmnn.xhtml#a10c50bc964cc8cc559eebcd7df5a8af3aa47abd1077ef632a38ada05b6edbf389">CapabilityClass::PaddingRequired</a>);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  BOOST_TEST(capabilities.empty());</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="comment">// No padding required for output</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  capabilities = handleFactory.GetCapabilities(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearmnn.xhtml#a10c50bc964cc8cc559eebcd7df5a8af3aa47abd1077ef632a38ada05b6edbf389">CapabilityClass::PaddingRequired</a>);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  BOOST_TEST(capabilities.empty());</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">// Padding required for Pooling2d</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  capabilities = handleFactory.GetCapabilities(pooling, output, <a class="code" href="namespacearmnn.xhtml#a10c50bc964cc8cc559eebcd7df5a8af3aa47abd1077ef632a38ada05b6edbf389">CapabilityClass::PaddingRequired</a>);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  BOOST_TEST(capabilities.size() == 1);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  BOOST_TEST((capabilities[0].m_CapabilityClass == <a class="code" href="namespacearmnn.xhtml#a10c50bc964cc8cc559eebcd7df5a8af3aa47abd1077ef632a38ada05b6edbf389">CapabilityClass::PaddingRequired</a>));</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  BOOST_TEST(capabilities[0].m_Value);</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"><a class="line" href="_neon_tensor_handle_tests_8cpp.xhtml#ae5d1b847e950dd7f6c77b2ae187faeeb"> 91</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(ConcatOnXorYSubTensorsNoPaddingRequiredTest)</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> {</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="comment">// Set up tensor infos</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> intermediateInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 3, 4, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">armnn::ElementwiseUnaryDescriptor</a> descriptor(<a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6">armnn::UnaryOperation::Abs</a>);</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>  <span class="comment">// Create the network</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> input0Layer = net->AddInputLayer(0, <span class="stringliteral">"input_0"</span>);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  input0Layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* elementwiseUnaryLayer0 = net->AddElementwiseUnaryLayer(descriptor, <span class="stringliteral">"elementwiseUnary_0"</span>);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  elementwiseUnaryLayer0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(intermediateInfo);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  input0Layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(elementwiseUnaryLayer0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> input1Layer = net->AddInputLayer(1, <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  input1Layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* elementwiseUnaryLayer1 = net->AddElementwiseUnaryLayer(descriptor, <span class="stringliteral">"elementwiseUnary_1"</span>);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  elementwiseUnaryLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(intermediateInfo);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  input1Layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(elementwiseUnaryLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  std::array<armnn::TensorShape, 2> concatInputShapes = { intermediateInfo.GetShape(), intermediateInfo.GetShape() };</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> concatLayer = net->AddConcatLayer(<a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a>(</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  concatInputShapes.begin(), concatInputShapes.end(), 2), <span class="stringliteral">"concatenation"</span>);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  elementwiseUnaryLayer0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  elementwiseUnaryLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = net->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  std::vector<armnn::BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a> };</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a> optimizedNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a>(*net, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& theGraph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optimizedNet.get());</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>  <span class="comment">// Load graph into runtime</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">armnn::NetworkId</a> networkIdentifier;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  runtime->LoadNetwork(networkIdentifier, std::move(optimizedNet));</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>  <span class="comment">// now check the concat how many sub-tensors it is using..</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">auto</span> TraceSubTensorHandleAncestry = [](<a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">armnn::ITensorHandle</a>* <span class="keyword">const</span> subTensorHandle)</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  {</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordflow">if</span> (subTensorHandle && subTensorHandle->GetParent())</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  {</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  }</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  };</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : theGraph)</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  {</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keywordflow">if</span>(layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">armnn::LayerType::Concat</a>)</div><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="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfSubTensors = 0;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < layer->GetNumInputSlots(); ++i)</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  {</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_output_slot.xhtml">armnn::OutputSlot</a>* slot = layer->GetInputSlot(i).GetConnectedOutputSlot();</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordflow">if</span> (TraceSubTensorHandleAncestry(slot-><a class="code" href="classarmnn_1_1_output_slot.xhtml#ab00cd1d8962a1927d0302901cb8410d7">GetOutputHandler</a>().<a class="code" href="classarmnn_1_1_output_handler.xhtml#afe3429ac30b180c11f01ea0f9f546f0e">GetData</a>()))</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  {</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  ++numberOfSubTensors;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  }</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  }</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="comment">// sub-tensors should be supported in this configuration</span></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numberOfSubTensors > 0);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  }</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  }</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> }</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div><div class="line"><a name="l00166"></a><span class="lineno"><a class="line" href="_neon_tensor_handle_tests_8cpp.xhtml#aee96173f56f248228ad10792039fe14d"> 166</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(ConcatonXorYPaddingRequiredTest)</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> {</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="comment">// Set up tensor infos</span></div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> intermediateInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 3, 4, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3;</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  descriptor.m_StrideX = descriptor.m_StrideY = 1;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  descriptor.m_PadLeft = 1;</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  descriptor.m_PadRight = 1;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  descriptor.m_PadTop = 1;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  descriptor.m_PadBottom = 1;</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  descriptor.m_PaddingMethod = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="comment">// Create the network</span></div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> input0Layer = net->AddInputLayer(0, <span class="stringliteral">"input_0"</span>);</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  input0Layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* pooling2dLayer0 = net->AddPooling2dLayer(descriptor, <span class="stringliteral">"pooling2d_0"</span>);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  pooling2dLayer0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(intermediateInfo);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  input0Layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(pooling2dLayer0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> input1Layer = net->AddInputLayer(1, <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  input1Layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* pooling2dLayer1 = net->AddPooling2dLayer(descriptor, <span class="stringliteral">"pooling2d_1"</span>);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  pooling2dLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(intermediateInfo);</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  input1Layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(pooling2dLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  std::array<armnn::TensorShape, 2> concatInputShapes = { intermediateInfo.GetShape(), intermediateInfo.GetShape() };</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> concatLayer = net->AddConcatLayer(<a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a>(</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  concatInputShapes.begin(), concatInputShapes.end(), 2), <span class="stringliteral">"concatenation"</span>);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  pooling2dLayer0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  pooling2dLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> </div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = net->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> </div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  std::vector<armnn::BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a> };</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a> optimizedNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a>(*net, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> </div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& theGraph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optimizedNet.get());</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="comment">// Load graph into runtime</span></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">armnn::NetworkId</a> networkIdentifier;</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  runtime->LoadNetwork(networkIdentifier, std::move(optimizedNet));</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> </div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="comment">// now check the concat how many sub-tensors it is using..</span></div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keyword">auto</span> TraceSubTensorHandleAncestry = [](<a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">armnn::ITensorHandle</a>* <span class="keyword">const</span> subTensorHandle)</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  {</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keywordflow">if</span> (subTensorHandle && subTensorHandle->GetParent())</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  {</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  }</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  };</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> </div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfSubTensors = 0;</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : theGraph)</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  {</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keywordflow">if</span>(layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">armnn::LayerType::Concat</a>)</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  {</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < layer->GetNumInputSlots(); ++i)</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  {</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_output_slot.xhtml">armnn::OutputSlot</a>* slot = layer->GetInputSlot(i).GetConnectedOutputSlot();</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keywordflow">if</span> (TraceSubTensorHandleAncestry(slot-><a class="code" href="classarmnn_1_1_output_slot.xhtml#ab00cd1d8962a1927d0302901cb8410d7">GetOutputHandler</a>().<a class="code" href="classarmnn_1_1_output_handler.xhtml#afe3429ac30b180c11f01ea0f9f546f0e">GetData</a>()))</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  {</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  ++numberOfSubTensors;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  }</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  }</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  }</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  }</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="comment">// sub-tensors should not be supported in this configuration</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numberOfSubTensors == 0);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> }</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> </div><div class="line"><a name="l00249"></a><span class="lineno"><a class="line" href="_neon_tensor_handle_tests_8cpp.xhtml#a9e77cd9397974a300d11aa0377182699"> 249</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(SplitteronXorYNoPaddingRequiredTest)</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> {</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> </div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis = 2;</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit = 2;</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = { 2, 3, 4, 2 };</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> intermediateInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 2, 3, 2, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keyword">const</span> std::vector<TensorShape> outputShapes{{ 2, 3, 2, 2 },</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  { 2, 3, 2, 2 }};</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> qScale = 1.0f;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keyword">const</span> int32_t qOffset = 0;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  std::vector<float> inputData{</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  1, 2,</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  3, 4,</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  5, 6,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  7, 8,</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  9, 10,</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  11, 12,</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  13, 14,</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  15, 16,</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  17, 18,</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  19, 20,</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  21, 22,</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  23, 24,</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  25, 26,</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  27, 28,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  29, 30,</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  31, 32,</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  33, 34,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  35, 36,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  37, 38,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  39, 40,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  41, 42,</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  43, 44,</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  45, 46,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  47, 48</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  };</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  std::vector<float> expectedOutput0{</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  1, 2,</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  3, 4,</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  9, 10,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  11, 12,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  17, 18,</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  19, 20,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  25, 26,</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  27, 28,</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  33, 34,</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  35, 36,</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  41, 42,</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  43, 44</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  };</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> </div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  std::vector<float> expectedOutput1{</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  5, 6,</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  7, 8,</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  13, 14,</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  15, 16,</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  21, 22,</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  23, 24,</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  29, 30,</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  31, 32,</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  37, 38,</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  39, 40,</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  45, 46,</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  47, 48</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  };</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> </div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, qScale, qOffset);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <a class="code" href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">armnn::ElementwiseUnaryDescriptor</a> descriptor(<a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6">armnn::UnaryOperation::Abs</a>);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span> </div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="comment">// Splitter</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  std::vector<unsigned int> splitterDimSizes(inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="comment">// Add current input shape to splitterDimSizes</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  {</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  splitterDimSizes[i] = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i];</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  }</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> </div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keywordflow">if</span> (splitterDimSizes[splitAxis] % numSplit != 0)</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  {</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Number of splits must evenly divide the dimension"</span>);</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  }</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> </div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  splitterDimSizes[splitAxis] /= numSplit;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a> splitDesc(numSplit, inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numSplit; ++g)</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  {</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0; dimIdx < splitterDimSizes.size(); ++dimIdx)</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  {</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(g, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  }</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  splitDesc.SetViewOriginCoord(g, splitAxis, splitterDimSizes[splitAxis] * g);</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  }</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* elementWiseUnary0 = net->AddElementwiseUnaryLayer(descriptor, <span class="stringliteral">"elementwiseunary_0"</span>);</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* elementWiseUnary1 = net->AddElementwiseUnaryLayer(descriptor, <span class="stringliteral">"elementwiseunary_0"</span>);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* splitter = net->AddSplitterLayer(splitDesc, <span class="stringliteral">"splitter"</span>);</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> </div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="comment">// Connections</span></div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, splitter, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(splitter, elementWiseUnary0, intermediateInfo, 0, 0);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(splitter, elementWiseUnary1, intermediateInfo, 1, 0);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  std::vector<IConnectableLayer*> pooling2dLayers{elementWiseUnary0, elementWiseUnary1};</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span> </div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < outputShapes.size(); ++i)</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  {</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outputShapes[i], <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, qScale, qOffset);</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(armnn::numeric_cast<LayerBindingId>(i));</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(pooling2dLayers[i], output, outputTensorInfo, 0, 0);</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  }</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> </div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  std::map<int, std::vector<float>> inputTensorData = {{ 0,inputData }};</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  std::map<int, std::vector<float>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }};</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span> </div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  std::vector<armnn::BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a> };</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a> optimizedNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a>(*net, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> </div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& theGraph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optimizedNet.get());</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> </div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="comment">// Load graph into runtime</span></div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">armnn::NetworkId</a> networkIdentifier;</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  runtime->LoadNetwork(networkIdentifier, std::move(optimizedNet));</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> </div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="comment">// now check the concat how many sub-tensors it is using..</span></div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keyword">auto</span> TraceSubTensorHandleAncestry = [](<a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">armnn::ITensorHandle</a>* <span class="keyword">const</span> subTensorHandle)</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  {</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordflow">if</span> (subTensorHandle && subTensorHandle->GetParent())</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  {</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  }</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  };</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> </div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : theGraph)</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  {</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keywordflow">if</span>(layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abcd30d7ea97ad20c2cddc0f47e6b70c7">armnn::LayerType::ElementwiseUnary</a>)</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  {</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfSubTensors = 0;</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < layer->GetNumInputSlots(); ++i)</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  {</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_output_slot.xhtml">armnn::OutputSlot</a>* slot = layer->GetInputSlot(i).GetConnectedOutputSlot();</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keywordflow">if</span> (TraceSubTensorHandleAncestry(slot-><a class="code" href="classarmnn_1_1_output_slot.xhtml#ab00cd1d8962a1927d0302901cb8410d7">GetOutputHandler</a>().<a class="code" href="classarmnn_1_1_output_handler.xhtml#afe3429ac30b180c11f01ea0f9f546f0e">GetData</a>()))</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  {</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  ++numberOfSubTensors;</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  }</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  }</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="comment">// sub-tensors should be supported in this configuration</span></div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numberOfSubTensors > 0);</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  }</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  }</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span> </div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors;</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  inputTensors.reserve(inputTensorData.size());</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : inputTensorData)</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  {</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  inputTensors.push_back({it.first,</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(runtime->GetInputTensorInfo(networkIdentifier, it.first), it.second.data())});</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  }</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors;</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  outputTensors.reserve(expectedOutputData.size());</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  std::map<int, std::vector<float>> outputStorage;</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : expectedOutputData)</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  {</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  std::vector<float> out(it.second.size());</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  outputStorage.emplace(it.first, out);</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  outputTensors.push_back({it.first,</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(runtime->GetOutputTensorInfo(networkIdentifier, it.first),</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  outputStorage.at(it.first).data())});</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  }</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> </div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="comment">// Does the inference.</span></div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  runtime->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> </div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="comment">// Checks the results.</span></div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="keywordtype">float</span> tolerance = 0.000001f;</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : expectedOutputData)</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  {</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  std::vector<float> out = outputStorage.at(it.first);</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < out.size(); ++i)</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  {</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  BOOST_CHECK_MESSAGE(Compare<armnn::DataType::Float32>(it.second[i], out[i], tolerance) == <span class="keyword">true</span>,</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="stringliteral">"Actual output: "</span> << out[i] << <span class="stringliteral">". Expected output:"</span> << it.second[i]);</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span> </div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  }</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  }</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> }</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span> </div><div class="line"><a name="l00453"></a><span class="lineno"><a class="line" href="_neon_tensor_handle_tests_8cpp.xhtml#a906b1ee6de2536e4683b104948fbe1b8"> 453</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(SplitteronXorYPaddingRequiredTest)</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> {</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> </div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis = 2;</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit = 2;</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> </div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = { 1, 1, 4, 4 };</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> intermediateInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 1, 2, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keyword">const</span> std::vector<TensorShape> outputShapes{{ 1, 1, 2, 4 },</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  { 1, 1, 2, 4 }};</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> </div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> qScale = 1.0f;</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keyword">const</span> int32_t qOffset = 0;</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> </div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  std::vector<float> inputData{</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  9.0f, 27.0f, 18.0f, 36.0f,</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  18.0f, 9.0f, 18.0f, 9.0f,</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  27.0f, 18.0f, 9.0f, 27.0f,</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  9.0f, 27.0f, 9.0f, 18.0f,</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  };</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> </div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  std::vector<float> expectedOutput0{</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  7.0f, 11.0f, 13.0f, 9.0f,</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  7.0f, 11.0f, 13.0f, 9.0f</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  };</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> </div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  std::vector<float> expectedOutput1{</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  9.0f, 11.0f, 12.0f, 7.0f,</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  9.0f, 11.0f, 12.0f, 7.0f</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  };</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span> </div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span> </div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, qScale, qOffset);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> </div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="comment">// Pooling</span></div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span> </div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="comment">// Splitter</span></div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  std::vector<unsigned int> splitterDimSizes(inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> </div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <span class="comment">// Add current input shape to splitterDimSizes</span></div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  {</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  splitterDimSizes[i] = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i];</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  }</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span> </div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="keywordflow">if</span> (splitterDimSizes[splitAxis] % numSplit != 0)</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  {</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Number of splits must evenly divide the dimension"</span>);</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  }</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> </div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  splitterDimSizes[splitAxis] /= numSplit;</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span> </div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a> splitDesc(numSplit, inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> </div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numSplit; ++g)</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  {</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0; dimIdx < splitterDimSizes.size(); ++dimIdx)</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  {</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(g, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  }</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  splitDesc.SetViewOriginCoord(g, splitAxis, splitterDimSizes[splitAxis] * g);</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  }</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> </div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling2d0 = net->AddPooling2dLayer(descriptor, <span class="stringliteral">"pooling2d_0"</span>);</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling2d1 = net->AddPooling2dLayer(descriptor, <span class="stringliteral">"pooling2d_1"</span>);</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* splitter = net->AddSplitterLayer(splitDesc, <span class="stringliteral">"splitter"</span>);</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span> </div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="comment">// Connections</span></div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, splitter, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(splitter, pooling2d0, intermediateInfo, 0, 0);</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(splitter, pooling2d1, intermediateInfo, 1, 0);</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> </div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  std::vector<IConnectableLayer*> pooling2dLayers{pooling2d0, pooling2d1};</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> </div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < outputShapes.size(); ++i)</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  {</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outputShapes[i], <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, qScale, qOffset);</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(armnn::numeric_cast<LayerBindingId>(i));</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(pooling2dLayers[i], output, outputTensorInfo, 0, 0);</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  }</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span> </div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  std::map<int, std::vector<float>> inputTensorData = {{ 0,inputData }};</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  std::map<int, std::vector<float>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }};</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span> </div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> </div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  std::vector<armnn::BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a> };</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a> optimizedNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a>(*net, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span> </div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& theGraph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optimizedNet.get());</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> </div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="comment">// Load graph into runtime</span></div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">armnn::NetworkId</a> networkIdentifier;</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  runtime->LoadNetwork(networkIdentifier, std::move(optimizedNet));</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span> </div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="comment">// now check the concat how many sub-tensors it is using..</span></div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="keyword">auto</span> TraceSubTensorHandleAncestry = [](<a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">armnn::ITensorHandle</a>* <span class="keyword">const</span> subTensorHandle)</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  {</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="keywordflow">if</span> (subTensorHandle && subTensorHandle->GetParent())</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  {</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  }</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  };</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span> </div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : theGraph)</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  {</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <span class="keywordflow">if</span>(layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001">armnn::LayerType::Pooling2d</a>)</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  {</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfSubTensors = 0;</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < layer->GetNumInputSlots(); ++i)</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  {</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_output_slot.xhtml">armnn::OutputSlot</a>* slot = layer->GetInputSlot(i).GetConnectedOutputSlot();</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="keywordflow">if</span> (TraceSubTensorHandleAncestry(slot-><a class="code" href="classarmnn_1_1_output_slot.xhtml#ab00cd1d8962a1927d0302901cb8410d7">GetOutputHandler</a>().<a class="code" href="classarmnn_1_1_output_handler.xhtml#afe3429ac30b180c11f01ea0f9f546f0e">GetData</a>()))</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  {</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  ++numberOfSubTensors;</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  }</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  }</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="comment">// sub-tensors should be supported in this configuration</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numberOfSubTensors == 0);</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  }</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  }</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> </div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors;</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  inputTensors.reserve(inputTensorData.size());</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : inputTensorData)</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  {</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  inputTensors.push_back({it.first,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(runtime->GetInputTensorInfo(networkIdentifier, it.first), it.second.data())});</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  }</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors;</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  outputTensors.reserve(expectedOutputData.size());</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  std::map<int, std::vector<float>> outputStorage;</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : expectedOutputData)</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  {</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  std::vector<float> out(it.second.size());</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  outputStorage.emplace(it.first, out);</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  outputTensors.push_back({it.first,</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(runtime->GetOutputTensorInfo(networkIdentifier, it.first),</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  outputStorage.at(it.first).data())});</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  }</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span> </div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <span class="comment">// Does the inference.</span></div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  runtime->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="comment">// Checks the results.</span></div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keywordtype">float</span> tolerance = 0.000001f;</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : expectedOutputData)</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  {</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  std::vector<float> out = outputStorage.at(it.first);</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < out.size(); ++i)</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  {</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  BOOST_CHECK_MESSAGE(Compare<armnn::DataType::Float32>(it.second[i], out[i], tolerance) == <span class="keyword">true</span>,</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="stringliteral">"Actual output: "</span> << out[i] << <span class="stringliteral">". Expected output:"</span> << it.second[i]);</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span> </div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  }</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  }</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span> }</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span> </div><div class="line"><a name="l00628"></a><span class="lineno"><a class="line" href="_neon_tensor_handle_tests_8cpp.xhtml#a9fbc89a3dc81976d70d76a101d659326"> 628</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(NeonTensorHandleFactoryMemoryManaged)</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span> {</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  std::shared_ptr<NeonMemoryManager> memoryManager = std::make_shared<NeonMemoryManager>(</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  std::make_unique<arm_compute::Allocator>(),</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <a class="code" href="classarmnn_1_1_base_memory_manager.xhtml#aaadc6dca70e0b3cc64ae0aba17be0aaeadfd0a82c4bf37b1e90b690a22a20692e">BaseMemoryManager::MemoryAffinity::Offset</a>);</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <a class="code" href="classarmnn_1_1_neon_tensor_handle_factory.xhtml">NeonTensorHandleFactory</a> handleFactory(memoryManager);</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 1, 2, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span> </div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <span class="comment">// create TensorHandle with memory managed</span></div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <span class="keyword">auto</span> handle = handleFactory.<a class="code" href="classarmnn_1_1_neon_tensor_handle_factory.xhtml#a36255ab20159b07024f505d5a58644d0">CreateTensorHandle</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, <span class="keyword">true</span>);</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  handle->Manage();</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  handle->Allocate();</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span> </div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  memoryManager->Acquire();</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  {</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <span class="keywordtype">float</span>* buffer = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(handle->Map());</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  BOOST_CHECK(buffer != <span class="keyword">nullptr</span>); <span class="comment">// Yields a valid pointer</span></div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  buffer[0] = 1.5f;</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  buffer[1] = 2.5f;</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  BOOST_CHECK(buffer[0] == 1.5f); <span class="comment">// Memory is writable and readable</span></div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  BOOST_CHECK(buffer[1] == 2.5f); <span class="comment">// Memory is writable and readable</span></div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  }</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  memoryManager->Release();</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span> </div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  memoryManager->Acquire();</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  {</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <span class="keywordtype">float</span>* buffer = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(handle->Map());</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  BOOST_CHECK(buffer != <span class="keyword">nullptr</span>); <span class="comment">// Yields a valid pointer</span></div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  buffer[0] = 3.5f;</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  buffer[1] = 4.5f;</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  BOOST_CHECK(buffer[0] == 3.5f); <span class="comment">// Memory is writable and readable</span></div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  BOOST_CHECK(buffer[1] == 4.5f); <span class="comment">// Memory is writable and readable</span></div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  }</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  memoryManager->Release();</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span> </div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <span class="keywordtype">float</span> testPtr[2] = { 2.5f, 5.5f };</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="comment">// Cannot import as import is disabled</span></div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  BOOST_CHECK_THROW(handle->Import(static_cast<void*>(testPtr), <a class="code" href="namespacearmnn.xhtml#a0fc99721e27eb20ecd0ea85a3cc8b488a1131a914388fac73e5f07b0ba0aad523">MemorySource::Malloc</a>), <a class="code" href="classarmnn_1_1_memory_import_exception.xhtml">MemoryImportException</a>);</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span> }</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span> </div><div class="line"><a name="l00668"></a><span class="lineno"><a class="line" href="_neon_tensor_handle_tests_8cpp.xhtml#a74c294cc4a68f61007953687f7f9c483"> 668</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(NeonTensorHandleFactoryImport)</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span> {</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  std::shared_ptr<NeonMemoryManager> memoryManager = std::make_shared<NeonMemoryManager>(</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  std::make_unique<arm_compute::Allocator>(),</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <a class="code" href="classarmnn_1_1_base_memory_manager.xhtml#aaadc6dca70e0b3cc64ae0aba17be0aaeadfd0a82c4bf37b1e90b690a22a20692e">BaseMemoryManager::MemoryAffinity::Offset</a>);</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <a class="code" href="classarmnn_1_1_neon_tensor_handle_factory.xhtml">NeonTensorHandleFactory</a> handleFactory(memoryManager);</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 1, 2, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span> </div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <span class="comment">// create TensorHandle without memory managed</span></div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <span class="keyword">auto</span> handle = handleFactory.<a class="code" href="classarmnn_1_1_neon_tensor_handle_factory.xhtml#a36255ab20159b07024f505d5a58644d0">CreateTensorHandle</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, <span class="keyword">false</span>);</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  handle->Manage();</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  handle->Allocate();</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  memoryManager->Acquire();</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span> </div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <span class="comment">// No buffer allocated when import is enabled</span></div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  BOOST_CHECK((PolymorphicDowncast<NeonTensorHandle*>(handle.get()))->GetTensor().buffer() == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span> </div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  <span class="keywordtype">float</span> testPtr[2] = { 2.5f, 5.5f };</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  <span class="comment">// Correctly import</span></div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  BOOST_CHECK(handle->Import(static_cast<void*>(testPtr), <a class="code" href="namespacearmnn.xhtml#a0fc99721e27eb20ecd0ea85a3cc8b488a1131a914388fac73e5f07b0ba0aad523">MemorySource::Malloc</a>));</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <span class="keywordtype">float</span>* buffer = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(handle->Map());</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  BOOST_CHECK(buffer != <span class="keyword">nullptr</span>); <span class="comment">// Yields a valid pointer after import</span></div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  BOOST_CHECK(buffer == testPtr); <span class="comment">// buffer is pointing to testPtr</span></div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <span class="comment">// Memory is writable and readable with correct value</span></div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  BOOST_CHECK(buffer[0] == 2.5f);</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  BOOST_CHECK(buffer[1] == 5.5f);</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  buffer[0] = 3.5f;</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  buffer[1] = 10.0f;</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  BOOST_CHECK(buffer[0] == 3.5f);</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  BOOST_CHECK(buffer[1] == 10.0f);</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  memoryManager->Release();</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span> }</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span> </div><div class="line"><a name="l00701"></a><span class="lineno"><a class="line" href="_neon_tensor_handle_tests_8cpp.xhtml#aba0196514960ec4a9047a4e2f93538c1"> 701</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(NeonTensorHandleSupportsInPlaceComputation)</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span> {</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  std::shared_ptr<NeonMemoryManager> memoryManager = std::make_shared<NeonMemoryManager>();</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <a class="code" href="classarmnn_1_1_neon_tensor_handle_factory.xhtml">NeonTensorHandleFactory</a> handleFactory(memoryManager);</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span> </div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <span class="comment">// NeonTensorHandleFactory supports InPlaceComputation</span></div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(handleFactory.SupportsInPlaceComputation());</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span> }</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span> </div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span> <a class="code" href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a>()</div><div class="ttc" id="_output_shape_of_squeeze_8cpp_xhtml_ae3a6cb217a792718f2bd0e8f45e3ca9e"><div class="ttname"><a href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)</div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4abcd30d7ea97ad20c2cddc0f47e6b70c7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abcd30d7ea97ad20c2cddc0f47e6b70c7">armnn::LayerType::ElementwiseUnary</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_runtime_xhtml_ad44ecd3700748dc30dc4bbe34ba5bde7"><div class="ttname"><a href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a></div><div class="ttdeci">static IRuntimePtr Create(const CreationOptions &options)</div><div class="ttdef"><b>Definition:</b> <a href="_runtime_8cpp_source.xhtml#l00037">Runtime.cpp:37</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">armnn::LayerType::Concat</a></div></div>
<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a></div><div class="ttdoc">A ViewsDescriptor for the SplitterLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00206">Descriptors.hpp:206</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00062">INetwork.hpp:62</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Pooling2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00371">Descriptors.hpp:371</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00187">Tensor.hpp:187</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Pooling2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00365">Descriptors.hpp:365</a></div></div>
<div class="ttc" id="classarmnn_1_1_neon_tensor_handle_factory_xhtml_a36255ab20159b07024f505d5a58644d0"><div class="ttname"><a href="classarmnn_1_1_neon_tensor_handle_factory.xhtml#a36255ab20159b07024f505d5a58644d0">armnn::NeonTensorHandleFactory::CreateTensorHandle</a></div><div class="ttdeci">std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo) const override</div><div class="ttdef"><b>Definition:</b> <a href="_neon_tensor_handle_factory_8cpp_source.xhtml#l00047">NeonTensorHandleFactory.cpp:47</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_memory_manager_xhtml_aaadc6dca70e0b3cc64ae0aba17be0aaeadfd0a82c4bf37b1e90b690a22a20692e"><div class="ttname"><a href="classarmnn_1_1_base_memory_manager.xhtml#aaadc6dca70e0b3cc64ae0aba17be0aaeadfd0a82c4bf37b1e90b690a22a20692e">armnn::BaseMemoryManager::MemoryAffinity::Offset</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6d8fb685cc1ff224f25aa127fcf62c86"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">armnn::Pooling2dDescriptor::m_PoolWidth</a></div><div class="ttdeci">uint32_t m_PoolWidth</div><div class="ttdoc">Pooling width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00373">Descriptors.hpp:373</a></div></div>
<div class="ttc" id="_neon_tensor_handle_factory_8hpp_xhtml"><div class="ttname"><a href="_neon_tensor_handle_factory_8hpp.xhtml">NeonTensorHandleFactory.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::SoftmaxDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Exponentiation value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00152">Descriptors.hpp:152</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a150468a02bd7b2d2d061c4aaaee939f0"><div class="ttname"><a href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a></div><div class="ttdeci">std::unique_ptr< IRuntime, void(*)(IRuntime *runtime)> IRuntimePtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00026">IRuntime.hpp:26</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a8c29d6ea9b4186d69aad5961c910939c"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">armnn::Pooling2dDescriptor::m_PaddingMethod</a></div><div class="ttdeci">PaddingMethod m_PaddingMethod</div><div class="ttdoc">The padding method to be used. (Exclude, IgnoreValue). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00383">Descriptors.hpp:383</a></div></div>
<div class="ttc" id="_neon_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_neon_tensor_handle_8hpp.xhtml">NeonTensorHandle.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6">armnn::ActivationFunction::Abs</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Pooling2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00369">Descriptors.hpp:369</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa01bce88f89975a5a031db4cc8861527"><div class="ttname"><a href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class ConstTensor > > InputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00340">Tensor.hpp:340</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a83015160d8c67d5d77735eb0d4033d9a"><div class="ttname"><a href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00020">IRuntime.hpp:20</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.dox:6</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Pooling2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00377">Descriptors.hpp:377</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</div></div>
<div class="ttc" id="_numeric_cast_8hpp_xhtml"><div class="ttname"><a href="_numeric_cast_8hpp.xhtml">NumericCast.hpp</a></div></div>
<div class="ttc" id="_common_test_utils_8hpp_xhtml"><div class="ttname"><a href="_common_test_utils_8hpp.xhtml">CommonTestUtils.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and a mutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00306">Tensor.hpp:306</a></div></div>
<div class="ttc" id="_polymorphic_downcast_8hpp_xhtml"><div class="ttname"><a href="_polymorphic_downcast_8hpp.xhtml">PolymorphicDowncast.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a5699e8606c37d18c03910b242cd1b010"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">armnn::Pooling2dDescriptor::m_PoolHeight</a></div><div class="ttdeci">uint32_t m_PoolHeight</div><div class="ttdoc">Pooling height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00375">Descriptors.hpp:375</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a0fc99721e27eb20ecd0ea85a3cc8b488a1131a914388fac73e5f07b0ba0aad523"><div class="ttname"><a href="namespacearmnn.xhtml#a0fc99721e27eb20ecd0ea85a3cc8b488a1131a914388fac73e5f07b0ba0aad523">armnn::MemorySource::Malloc</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Pooling2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00367">Descriptors.hpp:367</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a82e98ef05fd67036d1195ba17174d685"><div class="ttname"><a href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a></div><div class="ttdeci">IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptions &options=OptimizerOptions(), Optional< std::vector< std::string > &> messages=EmptyOptional())</div><div class="ttdoc">Create an optimized version of the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01502">Network.cpp:1502</a></div></div>
<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_aae0893695f5803a3517985c7cb1ccb2e"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">armnn::ViewsDescriptor::SetViewSize</a></div><div class="ttdeci">Status SetViewSize(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the size of the views. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00315">Descriptors.cpp:315</a></div></div>
<div class="ttc" id="_graph_8hpp_xhtml"><div class="ttname"><a href="_graph_8hpp.xhtml">Graph.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle.xhtml">armnn::ITensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_tensor_handle_8hpp_source.xhtml#l00015">ITensorHandle.hpp:15</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00314">Tensor.hpp:314</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a8f091a512915d1cb29a4ebf13dfc53ea"><div class="ttname"><a href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class Tensor > > OutputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00341">Tensor.hpp:341</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml">armnn::OutputSlot</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00083">Layer.hpp:83</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a674efcf6cbdb9e831d653ff0e821fb38"><div class="ttname"><a href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a></div><div class="ttdeci">std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00174">INetwork.hpp:174</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a5698be69cbd5dfe6c28fcd9867e8cbed"><div class="ttname"><a href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a></div><div class="ttdeci">#define ARMNN_ASSERT(COND)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00014">Assert.hpp:14</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001">armnn::LayerType::Pooling2d</a></div></div>
<div class="ttc" id="_graph_utils_8hpp_xhtml"><div class="ttname"><a href="_graph_utils_8hpp.xhtml">GraphUtils.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a10d15f3df1ab52b3b915a4be1dbf386b"><div class="ttname"><a href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">armnn::BOOST_AUTO_TEST_CASE</a></div><div class="ttdeci">BOOST_AUTO_TEST_CASE(CheckConvolution2dLayer)</div><div class="ttdef"><b>Definition:</b> <a href="_const_tensor_layer_visitor_8cpp_source.xhtml#l00268">ConstTensorLayerVisitor.cpp:268</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_handler_xhtml_afe3429ac30b180c11f01ea0f9f546f0e"><div class="ttname"><a href="classarmnn_1_1_output_handler.xhtml#afe3429ac30b180c11f01ea0f9f546f0e">armnn::OutputHandler::GetData</a></div><div class="ttdeci">ITensorHandle * GetData() const</div><div class="ttdoc">Gets the allocated tensor memory. </div><div class="ttdef"><b>Definition:</b> <a href="_output_handler_8hpp_source.xhtml#l00046">OutputHandler.hpp:46</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00029">Graph.hpp:29</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_runtime_1_1_creation_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00043">IRuntime.hpp:43</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a6a2659750d6161b693d0e51616791959"><div class="ttname"><a href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">armnn::GetGraphForTesting</a></div><div class="ttdeci">Graph & GetGraphForTesting(IOptimizedNetwork *optNet)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00025">TestUtils.cpp:25</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a10c50bc964cc8cc559eebcd7df5a8af3aa47abd1077ef632a38ada05b6edbf389"><div class="ttname"><a href="namespacearmnn.xhtml#a10c50bc964cc8cc559eebcd7df5a8af3aa47abd1077ef632a38ada05b6edbf389">armnn::CapabilityClass::PaddingRequired</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml"><div class="ttname"><a href="_assert_8hpp.xhtml">Assert.hpp</a></div></div>
<div class="ttc" id="_profiler_tests_8cpp_xhtml_af7f71af5c6c124222dd1c42c5df892f4"><div class="ttname"><a href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE_END()</div></div>
<div class="ttc" id="classarmnn_1_1_parse_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_parse_exception.xhtml">armnn::ParseException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00092">Exceptions.hpp:92</a></div></div>
<div class="ttc" id="structarmnn_1_1_elementwise_unary_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">armnn::ElementwiseUnaryDescriptor</a></div><div class="ttdoc">A ElementwiseUnaryDescriptor for the ElementwiseUnaryLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00098">Descriptors.hpp:98</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a0031997bf43bd2747656c31e4977793a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">armnn::Pooling2dDescriptor::m_PoolType</a></div><div class="ttdeci">PoolingAlgorithm m_PoolType</div><div class="ttdoc">The pooling algorithm to use (Max. Average, L2). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00363">Descriptors.hpp:363</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a></div><div class="ttdoc">The padding fields count, but are ignored. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a></div><div class="ttdoc">CPU Execution: NEON: ArmCompute. </div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdoc">Function that returns the tensor rank. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00174">Tensor.cpp:174</a></div></div>
<div class="ttc" id="classarmnn_1_1_memory_import_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_memory_import_exception.xhtml">armnn::MemoryImportException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00125">Exceptions.hpp:125</a></div></div>
<div class="ttc" id="_network_8hpp_xhtml"><div class="ttname"><a href="_network_8hpp.xhtml">Network.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_ab00cd1d8962a1927d0302901cb8410d7"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#ab00cd1d8962a1927d0302901cb8410d7">armnn::OutputSlot::GetOutputHandler</a></div><div class="ttdeci">const OutputHandler & GetOutputHandler() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00119">Layer.hpp:119</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div>
<div class="ttc" id="_test_utils_8cpp_xhtml_a0b295acb179f15eb3fb862b32008f782"><div class="ttname"><a href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a></div><div class="ttdeci">void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00012">TestUtils.cpp:12</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a733ae6b70d0bfa43433c3e7606992328"><div class="ttname"><a href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a></div><div class="ttdeci">OriginsDescriptor CreateDescriptorForConcatenation(TensorShapeIt first, TensorShapeIt last, unsigned int concatenationDimension)</div><div class="ttdoc">Convenience template to create an OriginsDescriptor to use when creating a ConcatLayer for performing...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00258">Descriptors.hpp:258</a></div></div>
<div class="ttc" id="classarmnn_1_1_neon_tensor_handle_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_neon_tensor_handle_factory.xhtml">armnn::NeonTensorHandleFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_neon_tensor_handle_factory_8hpp_source.xhtml#l00034">NeonTensorHandleFactory.hpp:34</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00173">INetwork.hpp:173</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00329">Descriptors.hpp:329</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00510">Network.cpp:510</a></div></div>
<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a></div><div class="ttdoc">A SoftmaxDescriptor for the SoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00139">Descriptors.hpp:139</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Pooling2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00379">Descriptors.hpp:379</a></div></div>
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