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<div class="title">PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp</div> </div>
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<a href="_permute_and_batch_to_space_as_depth_to_space_tests_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment">// Copyright © 2019 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="preprocessor">#include &quot;../TestUtils.hpp&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_network_8hpp.html">Network.hpp</a>&gt;</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_optimizer_8hpp.html">Optimizer.hpp</a>&gt;</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="preprocessor">#include &lt;boost/test/unit_test.hpp&gt;</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearmnn.html">armnn</a>;</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<a class="code" href="_output_shape_of_squeeze_8cpp.html#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(<a class="code" href="classarmnn_1_1_optimizer.html">Optimizer</a>)</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearmnn_1_1optimizations.html">armnn::optimizations</a>;</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;{</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;<a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> CreateTestNetwork()</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;{</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160; <span class="comment">// Create a network</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160; <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.html#a706f7345af3f18f4b16e226a672214c6">INetwork::Create</a>();</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160; <span class="keyword">auto</span> input = network-&gt;AddInputLayer(0, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> inputInfo({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160; input-&gt;GetOutputSlot(0).SetTensorInfo(inputInfo);</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160; <span class="comment">// Insert Permute which swaps batches and channels dimensions</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; <span class="keyword">auto</span> permute = network-&gt;AddPermuteLayer(<a class="code" href="structarmnn_1_1_permute_descriptor.html">PermuteDescriptor</a>(<a class="code" href="classarmnn_1_1_permutation_vector.html">PermutationVector</a>{ 3, 1, 2, 0 }), <span class="stringliteral">&quot;permute&quot;</span>);</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> permuteInfo({ 4, 2, 3, 1 }, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; permute-&gt;GetOutputSlot(0).SetTensorInfo(permuteInfo);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; input-&gt;GetOutputSlot(0).Connect(permute-&gt;GetInputSlot(0));</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; <span class="comment">// Insert BatchToSpace</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.html">BatchToSpaceNdDescriptor</a> batchToSpaceDesc;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.html#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = { 2, 2 };</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <span class="keyword">auto</span> batchToSpace = network-&gt;AddBatchToSpaceNdLayer(batchToSpaceDesc, <span class="stringliteral">&quot;batchToSpace&quot;</span>);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> batchToSpaceInfo({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; batchToSpace-&gt;GetOutputSlot(0).SetTensorInfo(batchToSpaceInfo);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; permute-&gt;GetOutputSlot(0).Connect(batchToSpace-&gt;GetInputSlot(0));</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; <span class="keyword">auto</span> output = network-&gt;AddOutputLayer(0, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; batchToSpace-&gt;GetOutputSlot(0).Connect(output-&gt;GetInputSlot(0));</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160;}</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160;} <span class="comment">// namespace</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160;</div><div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="_permute_and_batch_to_space_as_depth_to_space_tests_8cpp.html#ac9ae3545393ad3f2dac5c8f789bbbfbf"> 56</a></span>&#160;<a class="code" href="namespacearmnn.html#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(PermuteAndBatchToSpaceAsDepthToSpaceOptimizerTest)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160;{</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = CreateTestNetwork();</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <a class="code" href="classarmnn_1_1_graph.html">Graph</a> graph = <span class="keyword">static_cast&lt;</span><a class="code" href="classarmnn_1_1_network.html">Network</a>*<span class="keyword">&gt;</span>(network.get())-&gt;GetGraph();</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <span class="comment">// Confirm initial graph is as we expect</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; BOOST_TEST(<a class="code" href="_test_utils_8hpp.html#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.html#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.html#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(), &amp;IsLayerOfType&lt;InputLayer&gt;, &amp;IsLayerOfType&lt;PermuteLayer&gt;,</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; &amp;IsLayerOfType&lt;BatchToSpaceNdLayer&gt;, &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="comment">// Perform the optimization which should merge the two layers into a DepthToSpace</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <a class="code" href="classarmnn_1_1_optimizer.html#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.html#aa7427025a851113a492de0b68b23d22a">MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.html#a820df8da5229f50ca7d4d11cb74def2c">PermuteAndBatchToSpaceAsDepthToSpace</a>()));</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <span class="comment">// Check that the replacement has been made as expected</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <span class="keyword">auto</span> checkDepthToSpace = [](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.html">Layer</a>* <span class="keyword">const</span> layer) -&gt; <span class="keywordtype">bool</span> {</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; <span class="keywordflow">return</span> IsLayerOfType&lt;DepthToSpaceLayer&gt;(layer) &amp;&amp;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; static_cast&lt;const DepthToSpaceLayer*&gt;(layer)-&gt;GetParameters().m_BlockSize == 2 &amp;&amp;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; <span class="keyword">static_cast&lt;</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_depth_to_space_layer.html">DepthToSpaceLayer</a>*<span class="keyword">&gt;</span>(layer)-&gt;GetParameters().m_DataLayout == <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a> &amp;&amp;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; layer-&gt;GetOutputHandler().GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; };</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; BOOST_TEST(<a class="code" href="_test_utils_8hpp.html#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.html#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.html#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(), &amp;IsLayerOfType&lt;InputLayer&gt;, checkDepthToSpace,</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; <span class="comment">// Check the new layer has the two merged layers listed as related layers</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; std::list&lt;std::string&gt; testRelatedLayers = { <span class="stringliteral">&quot;batchToSpace&quot;</span>, <span class="stringliteral">&quot;permute&quot;</span> };</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; BOOST_TEST(CheckRelatedLayers&lt;DepthToSpaceLayer&gt;(graph, testRelatedLayers));</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160;}</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;<span class="comment">// This unit test needs the reference backend, it&#39;s not available if the reference backend is not built</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160;<span class="preprocessor">#if defined(ARMNNREF_ENABLED)</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160;<a class="code" href="namespacearmnn.html#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(PermuteAndBatchToSpaceAsDepthToSpaceCorrectnessTest)</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160;{</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = CreateTestNetwork();</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; <a class="code" href="namespacearmnn.html#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime = <a class="code" href="classarmnn_1_1_i_runtime.html#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.html">IRuntime::CreationOptions</a>());</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; <a class="code" href="namespacearmnn.html#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optimizedNetwork = <a class="code" href="namespacearmnn.html#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, { <a class="code" href="namespacearmnn.html#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a> }, runtime-&gt;GetDeviceSpec());</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <span class="comment">// Confirm that the optimization has actually taken place</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_graph.html">Graph</a>&amp; optGraph = <span class="keyword">static_cast&lt;</span><a class="code" href="classarmnn_1_1_optimized_network.html">OptimizedNetwork</a>*<span class="keyword">&gt;</span>(optimizedNetwork.get())-&gt;GetGraph();</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; BOOST_TEST(<a class="code" href="_test_utils_8hpp.html#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(optGraph.cbegin(), optGraph.cend(), &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; &amp;IsLayerOfType&lt;DepthToSpaceLayer&gt;, &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; <span class="comment">// Load the graph into a runtime so we can check it produces the correct output</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; <a class="code" href="namespacearmnn.html#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; runtime-&gt;LoadNetwork(netId, std::move(optimizedNetwork));</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; std::vector&lt;float&gt; inputData{</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; <span class="comment">// Each row here is a row of pixels where each pixel has 4 channels</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <span class="comment">// clang-format off</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; 1.0f, 2.0f, 3.0f, 4.0f, 10.0f, 20.0f, 30.0f, 40.0f, 100.0f, 200.0f, 300.0f, 400.0f,</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; -1.0f, -2.0f, -3.0f, -4.0f, -10.0f, -20.0f, -30.0f, -40.0f, -100.0f, -200.0f, -300.0f, -400.0f,</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <span class="comment">// clang-format on</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; };</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a> input(<a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), inputData);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; <a class="code" href="namespacearmnn.html#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputs = { { 0, input } };</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; std::vector&lt;float&gt; outputData(4 * 6);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; <a class="code" href="classarmnn_1_1_tensor.html">Tensor</a> output(<a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), outputData.data());</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <a class="code" href="namespacearmnn.html#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputs = { { 0, output } };</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; runtime-&gt;EnqueueWorkload(netId, inputs, outputs);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; <span class="comment">// Check the output is as expected.</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="comment">// Note this output has been generated by running the network *without* the optimization.</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; std::vector&lt;float&gt; expectedOutput = {</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; <span class="comment">// Rows and columns here match exactly with the tensor, as there is only 1 channel.</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; <span class="comment">// clang-format off</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; 1.0f, 2.0f, 10.0f, 20.0f, 100.0f, 200.0f,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; 3.0f, 4.0f, 30.0f, 40.0f, 300.0f, 400.0f,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; -1.0f, -2.0f, -10.0f, -20.0f, -100.0f, -200.0f,</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; -3.0f, -4.0f, -30.0f, -40.0f, -300.0f, -400.0f,</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <span class="comment">// clang-format on</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; };</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; BOOST_TEST(outputData == expectedOutput);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160;}</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160;<span class="preprocessor">#endif</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160;<a class="code" href="_profiler_tests_8cpp.html#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a>()</div><div class="ttc" id="classarmnn_1_1_depth_to_space_layer_html"><div class="ttname"><a href="classarmnn_1_1_depth_to_space_layer.html">armnn::DepthToSpaceLayer</a></div><div class="ttdoc">This layer represents a DepthToSpace operation. </div><div class="ttdef"><b>Definition:</b> <a href="_depth_to_space_layer_8hpp_source.html#l00014">DepthToSpaceLayer.hpp:14</a></div></div>
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<div class="ttc" id="classarmnn_1_1_const_tensor_html"><div class="ttname"><a href="classarmnn_1_1_const_tensor.html">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.html#l00199">Tensor.hpp:199</a></div></div>
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<div class="ttc" id="namespacearmnn_html_a82e98ef05fd67036d1195ba17174d685"><div class="ttname"><a href="namespacearmnn.html#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a></div><div class="ttdeci">IOptimizedNetworkPtr Optimize(const INetwork &amp;network, const std::vector&lt; BackendId &gt; &amp;backendPreferences, const IDeviceSpec &amp;deviceSpec, const OptimizerOptions &amp;options=OptimizerOptions(), Optional&lt; std::vector&lt; std::string &gt; &amp;&gt; messages=EmptyOptional())</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.html#l00807">Network.cpp:807</a></div></div>
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<div class="ttc" id="classarmnn_1_1_layer_html"><div class="ttname"><a href="classarmnn_1_1_layer.html">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.html#l00209">Layer.hpp:209</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00053">Tensor.hpp:53</a></div></div>
<div class="ttc" id="namespacearmnn_html_a10d15f3df1ab52b3b915a4be1dbf386b"><div class="ttname"><a href="namespacearmnn.html#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.html#l00170">ConstTensorLayerVisitor.cpp:170</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_html_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.html#a6089e1ca91914015777ea780a513131a">armnn::BatchToSpaceNdDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00684">Descriptors.hpp:684</a></div></div>
<div class="ttc" id="_network_8hpp_html"><div class="ttname"><a href="_network_8hpp.html">Network.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_optimizer_html_a1f48ba622b76ea04d15c9b62f642bf08"><div class="ttname"><a href="classarmnn_1_1_optimizer.html#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a></div><div class="ttdeci">static void Pass(Graph &amp;graph, const Optimizations &amp;optimizations)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8cpp_source.html#l00016">Optimizer.cpp:16</a></div></div>
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<div class="ttc" id="namespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_html"><div class="ttname"><a href="namespacearmnn_1_1optimizations.html">armnn::optimizations</a></div><div class="ttdef"><b>Definition:</b> <a href="_add_debug_8hpp_source.html#l00012">AddDebug.hpp:12</a></div></div>
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<div class="ttc" id="classarmnn_1_1_network_html"><div class="ttname"><a href="classarmnn_1_1_network.html">armnn::Network</a></div><div class="ttdoc">Private implementation of INetwork. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8hpp_source.html#l00027">Network.hpp:27</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_html"><div class="ttname"><a href="classarmnn_1_1_tensor.html">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.html#l00191">Tensor.hpp:191</a></div></div>
<div class="ttc" id="namespacearmnn_html_aa01bce88f89975a5a031db4cc8861527"><div class="ttname"><a href="namespacearmnn.html#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a></div><div class="ttdeci">std::vector&lt; std::pair&lt; LayerBindingId, class ConstTensor &gt; &gt; InputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00225">Tensor.hpp:225</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_network_html_a706f7345af3f18f4b16e226a672214c6"><div class="ttname"><a href="classarmnn_1_1_i_network.html#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create()</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.html#l00048">Network.cpp:48</a></div></div>
<div class="ttc" id="namespacearmnn_html"><div class="ttname"><a href="namespacearmnn.html">armnn</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_helper_8hpp_source.html#l00011">BackendHelper.hpp:11</a></div></div>
<div class="ttc" id="namespacearmnn_html_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr&lt; INetwork, void(*)(INetwork *network)&gt; INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.html#l00085">INetwork.hpp:85</a></div></div>
<div class="ttc" id="namespacearmnn_html_ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64"><div class="ttname"><a href="namespacearmnn.html#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a></div><div class="ttdoc">CPU Execution: Reference C++ kernels. </div></div>
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<div class="ttc" id="classarmnn_1_1_graph_html_a98b1109a9006f8cc7d4566146a3bd737"><div class="ttname"><a href="classarmnn_1_1_graph.html#a98b1109a9006f8cc7d4566146a3bd737">armnn::Graph::cbegin</a></div><div class="ttdeci">ConstIterator cbegin() const</div><div class="ttdoc">Returns const iterator pointing to the beginning of the list. Lowercase for range-based for loops...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.html#l00168">Graph.hpp:168</a></div></div>
<div class="ttc" id="namespacearmnn_html_aa7427025a851113a492de0b68b23d22a"><div class="ttname"><a href="namespacearmnn.html#aa7427025a851113a492de0b68b23d22a">armnn::MakeOptimizations</a></div><div class="ttdeci">Optimizer::Optimizations MakeOptimizations(Args &amp;&amp;... args)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8hpp_source.html#l00043">Optimizer.hpp:43</a></div></div>
<div class="ttc" id="_profiler_tests_8cpp_html_af7f71af5c6c124222dd1c42c5df892f4"><div class="ttname"><a href="_profiler_tests_8cpp.html#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE_END()</div></div>
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<div class="ttc" id="classarmnn_1_1_graph_html"><div class="ttname"><a href="classarmnn_1_1_graph.html">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.html#l00029">Graph.hpp:29</a></div></div>
<div class="ttc" id="_output_shape_of_squeeze_8cpp_html_ae3a6cb217a792718f2bd0e8f45e3ca9e"><div class="ttname"><a href="_output_shape_of_squeeze_8cpp.html#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)</div></div>
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<div class="ttc" id="classarmnn_1_1_graph_html_a02fd29b6dc3e21fbe4484362d85893bc"><div class="ttname"><a href="classarmnn_1_1_graph.html#a02fd29b6dc3e21fbe4484362d85893bc">armnn::Graph::cend</a></div><div class="ttdeci">ConstIterator cend() const</div><div class="ttdoc">Returns const iterator pointing to the end of the list. Lowercase for range-based for loops...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.html#l00170">Graph.hpp:170</a></div></div>
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<div class="ttc" id="namespacearmnn_html_a83015160d8c67d5d77735eb0d4033d9a"><div class="ttname"><a href="namespacearmnn.html#a83015160d8c67d5d77735eb0d4033d9a">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.html#l00019">IRuntime.hpp:19</a></div></div>
<div class="ttc" id="namespacearmnn_html_a150468a02bd7b2d2d061c4aaaee939f0"><div class="ttname"><a href="namespacearmnn.html#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a></div><div class="ttdeci">std::unique_ptr&lt; IRuntime, void(*)(IRuntime *runtime)&gt; IRuntimePtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.html#l00024">IRuntime.hpp:24</a></div></div>
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<div class="ttc" id="namespacearmnn_html_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
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