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| author | Nikhil Raj <nikhil.raj@arm.com> | 2022-08-19 15:23:36 +0100 |
|---|---|---|
| committer | Nikhil Raj <nikhil.raj@arm.com> | 2022-08-19 15:23:36 +0100 |
| commit | 7bfd38a721360183f3392f9ab35db18a0dd7fef8 (patch) | |
| tree | 5b4da2f2e88636c939afbafa2571170297114e40 /22.08/_permute_and_batch_to_space_as_depth_to_space_tests_8cpp.xhtml | |
| parent | d5d43d82c0137e08553e44345c609cdd1a7931c7 (diff) | |
| download | armnn-7bfd38a721360183f3392f9ab35db18a0dd7fef8.tar.gz | |
Update Doxygen for 22.08 Release
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: I4789fe868e0492839be1482e5cee3642ed90d756
Diffstat (limited to '22.08/_permute_and_batch_to_space_as_depth_to_space_tests_8cpp.xhtml')
| -rw-r--r-- | 22.08/_permute_and_batch_to_space_as_depth_to_space_tests_8cpp.xhtml | 190 |
1 files changed, 190 insertions, 0 deletions
diff --git a/22.08/_permute_and_batch_to_space_as_depth_to_space_tests_8cpp.xhtml b/22.08/_permute_and_batch_to_space_as_depth_to_space_tests_8cpp.xhtml new file mode 100644 index 0000000000..9232e2d23b --- /dev/null +++ b/22.08/_permute_and_batch_to_space_as_depth_to_space_tests_8cpp.xhtml @@ -0,0 +1,190 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: src/armnn/test/optimizations/PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp File Reference</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="jquery.js"></script> +<script type="text/javascript" src="dynsections.js"></script> +<link href="navtree.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="resize.js"></script> +<script type="text/javascript" src="navtreedata.js"></script> +<script type="text/javascript" src="navtree.js"></script> +<script type="text/javascript"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! 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Note this does not ensure the correctness of the optimization - that is done in the below test.</p> +<p>Tests that the optimization performed by PermuteAndBatchToSpaceAsDepthToSpace is as expected. Note this does not ensure the correctness of the optimization - that is done in the below test. </p> + +<p class="definition">Definition at line <a class="el" href="_permute_and_batch_to_space_as_depth_to_space_tests_8cpp_source.xhtml#l00015">15</a> of file <a class="el" href="_permute_and_batch_to_space_as_depth_to_space_tests_8cpp_source.xhtml">PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp</a>.</p> + +<p class="reference">References <a class="el" href="_graph_8hpp_source.xhtml#l00179">Graph::cbegin()</a>, <a class="el" href="_graph_8hpp_source.xhtml#l00181">Graph::cend()</a>, <a class="el" href="est_utils_2_test_utils_8hpp_source.xhtml#l00021">CheckSequence()</a>, <a class="el" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::CpuRef</a>, <a class="el" href="_runtime_8cpp_source.xhtml#l00049">IRuntime::Create()</a>, <a class="el" href="_network_8cpp_source.xhtml#l00475">INetwork::Create()</a>, <a class="el" href="_cl_custom_allocator_tests_8cpp_source.xhtml#l00062">CreateTestNetwork()</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="_test_utils_8cpp_source.xhtml#l00049">armnn::GetGraphForTesting()</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00859">BatchToSpaceNdDescriptor::m_BlockShape</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00863">BatchToSpaceNdDescriptor::m_DataLayout</a>, <a class="el" href="_optimizer_8hpp_source.xhtml#l00043">armnn::MakeOptimizations()</a>, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>, <a class="el" href="_network_8cpp_source.xhtml#l01864">armnn::Optimize()</a>, and <a class="el" href="_optimizer_8cpp_source.xhtml#l00016">Optimizer::Pass()</a>.</p> +<div class="fragment"><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> {</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn_1_1optimizations.xhtml">armnn::optimizations</a>;</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="keyword">namespace</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> <span class="comment"></span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment">/// Shared function for the below tests, so that we test the same network in both cases.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"></span>std::unique_ptr<NetworkImpl> CreateTestNetworkImpl()</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> {</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  std::unique_ptr<NetworkImpl> network(<span class="keyword">new</span> <a class="code" href="classarmnn_1_1_network_impl.xhtml">NetworkImpl</a>());</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">auto</span> input = network->AddInputLayer(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  input->GetOutputSlot(0).SetTensorInfo(inputInfo);</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">// Insert Permute which swaps batches and channels dimensions</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keyword">auto</span> permute = network->AddPermuteLayer(<a class="code" href="structarmnn_1_1_permute_descriptor.xhtml">PermuteDescriptor</a>(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>{ 3, 1, 2, 0 }), <span class="stringliteral">"permute"</span>);</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> permuteInfo({ 4, 2, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  permute->GetOutputSlot(0).SetTensorInfo(permuteInfo);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  input->GetOutputSlot(0).Connect(permute->GetInputSlot(0));</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="comment">// Insert BatchToSpace</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">BatchToSpaceNdDescriptor</a> batchToSpaceDesc;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = { 2, 2 };</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keyword">auto</span> batchToSpace = network->AddBatchToSpaceNdLayer(batchToSpaceDesc, <span class="stringliteral">"batchToSpace"</span>);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> batchToSpaceInfo({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  batchToSpace->GetOutputSlot(0).SetTensorInfo(batchToSpaceInfo);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  permute->GetOutputSlot(0).Connect(batchToSpace->GetInputSlot(0));</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">auto</span> output = network->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  batchToSpace->GetOutputSlot(0).Connect(output->GetInputSlot(0));</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> }</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="comment"></span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="comment">/// Shared function for the below tests, so that we test the same network in both cases.</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="comment"></span>std::unique_ptr<NetworkImpl> CreateTransposeTestNetworkImpl()</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="comment">// Create a network</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  std::unique_ptr<NetworkImpl> network(<span class="keyword">new</span> <a class="code" href="classarmnn_1_1_network_impl.xhtml">NetworkImpl</a>());</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">auto</span> input = network->AddInputLayer(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  input->GetOutputSlot(0).SetTensorInfo(inputInfo);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="comment">// Insert Permute which swaps batches and channels dimensions</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keyword">auto</span> permute = network->AddTransposeLayer(<a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">TransposeDescriptor</a>(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>{ 3, 1, 2, 0 }), <span class="stringliteral">"permute"</span>);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> permuteInfo({ 4, 2, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  permute->GetOutputSlot(0).SetTensorInfo(permuteInfo);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  input->GetOutputSlot(0).Connect(permute->GetInputSlot(0));</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="comment">// Insert BatchToSpace</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">BatchToSpaceNdDescriptor</a> batchToSpaceDesc;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = { 2, 2 };</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keyword">auto</span> batchToSpace = network->AddBatchToSpaceNdLayer(batchToSpaceDesc, <span class="stringliteral">"batchToSpace"</span>);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> batchToSpaceInfo({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  batchToSpace->GetOutputSlot(0).SetTensorInfo(batchToSpaceInfo);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  permute->GetOutputSlot(0).Connect(batchToSpace->GetInputSlot(0));</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keyword">auto</span> output = network->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  batchToSpace->GetOutputSlot(0).Connect(output->GetInputSlot(0));</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="keywordflow">return</span> network;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> }</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="comment"></span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> <span class="comment">/// Tests that the optimization performed by PermuteAndBatchToSpaceAsDepthToSpace is as expected.</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="comment">/// Note this does not ensure the correctness of the optimization - that is done in the below test.</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> <span class="comment"></span>TEST_CASE(<span class="stringliteral">"PermuteAndBatchToSpaceAsDepthToSpaceOptimizerTest"</span>)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> {</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  std::unique_ptr<NetworkImpl> network = CreateTestNetworkImpl();</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph = network.get()->GetGraph();</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="comment">// Confirm initial graph is as we expect</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  CHECK(<a class="code" href="est_utils_2_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(), &IsLayerOfType<InputLayer>, &IsLayerOfType<PermuteLayer>,</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  &IsLayerOfType<BatchToSpaceNdLayer>, &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="comment">// Perform the optimization which should merge the two layers into a DepthToSpace</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a17d1279f5f8e3b92c328b1ed3b6fd549">PermuteAndBatchToSpaceAsDepthToSpace</a>()));</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="comment">// Check that the replacement has been made as expected</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keyword">auto</span> checkDepthToSpace = [](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer) -> <span class="keywordtype">bool</span> {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">return</span> IsLayerOfType<DepthToSpaceLayer>(layer) &&</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  static_cast<const DepthToSpaceLayer*>(layer)->GetParameters().m_BlockSize == 2 &&</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_depth_to_space_layer.xhtml">DepthToSpaceLayer</a>*<span class="keyword">></span>(layer)->GetParameters().m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a> &&</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  layer->GetOutputHandler().GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  };</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  CHECK(<a class="code" href="est_utils_2_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(), &IsLayerOfType<InputLayer>, checkDepthToSpace,</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="comment">// Check the new layer has the two merged layers listed as related layers</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  std::list<std::string> testRelatedLayers = { <span class="stringliteral">"batchToSpace"</span>, <span class="stringliteral">"permute"</span> };</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  CHECK(CheckRelatedLayers<DepthToSpaceLayer>(graph, testRelatedLayers));</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> }</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="comment"></span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="comment">/// Tests that the optimization performed by PermuteAndBatchToSpaceAsDepthToSpace is as expected.</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="comment">/// Note this does not ensure the correctness of the optimization - that is done in the below test.</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="comment"></span>TEST_CASE(<span class="stringliteral">"TransposeAndBatchToSpaceAsDepthToSpaceOptimizerTest"</span>)</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> {</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  std::unique_ptr<NetworkImpl> network = CreateTransposeTestNetworkImpl();</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph = network.get()->GetGraph();</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="comment">// Confirm initial graph is as we expect</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  CHECK(<a class="code" href="est_utils_2_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(), &IsLayerOfType<InputLayer>, &IsLayerOfType<TransposeLayer>,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  &IsLayerOfType<BatchToSpaceNdLayer>, &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="comment">// Perform the optimization which should merge the two layers into a DepthToSpace</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a98f54d4391347d517c7a7869e7707203">TransposeAndBatchToSpaceAsDepthToSpace</a>()));</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> </div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// Check that the replacement has been made as expected</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keyword">auto</span> checkDepthToSpace = [](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer) -> <span class="keywordtype">bool</span> {</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">return</span> IsLayerOfType<DepthToSpaceLayer>(layer) &&</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  static_cast<const DepthToSpaceLayer*>(layer)->GetParameters().m_BlockSize == 2 &&</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_depth_to_space_layer.xhtml">DepthToSpaceLayer</a>*<span class="keyword">></span>(layer)->GetParameters().m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a> &&</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  layer->GetOutputHandler().GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  };</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>  CHECK(<a class="code" href="est_utils_2_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(), &IsLayerOfType<InputLayer>, checkDepthToSpace,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  &IsLayerOfType<OutputLayer>));</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="comment">// Check the new layer has the two merged layers listed as related layers</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  std::list<std::string> testRelatedLayers = { <span class="stringliteral">"batchToSpace"</span>, <span class="stringliteral">"permute"</span> };</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  CHECK(CheckRelatedLayers<DepthToSpaceLayer>(graph, testRelatedLayers));</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> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="comment">// This unit test needs the reference backend, it's not available if the reference backend is not built</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="preprocessor">#if defined(ARMNNREF_ENABLED)</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="comment"></span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="comment">/// Shared function for the below tests, so that we test the same network in both cases.</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> <span class="comment"></span><a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="_cl_custom_allocator_tests_8cpp.xhtml#a15610ca027aa31bdc7f39133cd79359a">CreateTestNetwork</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="comment">// Create a network</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</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="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keyword">auto</span> input = network->AddInputLayer(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  input->GetOutputSlot(0).SetTensorInfo(inputInfo);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="comment">// Insert Permute which swaps batches and channels dimensions</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">auto</span> permute = network->AddPermuteLayer(<a class="code" href="structarmnn_1_1_permute_descriptor.xhtml">PermuteDescriptor</a>(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>{ 3, 1, 2, 0 }), <span class="stringliteral">"permute"</span>);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> permuteInfo({ 4, 2, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  permute->GetOutputSlot(0).SetTensorInfo(permuteInfo);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  input->GetOutputSlot(0).Connect(permute->GetInputSlot(0));</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>  <span class="comment">// Insert BatchToSpace</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">BatchToSpaceNdDescriptor</a> batchToSpaceDesc;</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = { 2, 2 };</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keyword">auto</span> batchToSpace = network->AddBatchToSpaceNdLayer(batchToSpaceDesc, <span class="stringliteral">"batchToSpace"</span>);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> batchToSpaceInfo({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  batchToSpace->GetOutputSlot(0).SetTensorInfo(batchToSpaceInfo);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  permute->GetOutputSlot(0).Connect(batchToSpace->GetInputSlot(0));</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keyword">auto</span> output = network->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  batchToSpace->GetOutputSlot(0).Connect(output->GetInputSlot(0));</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> </div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> }</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> <span class="comment"></span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> <span class="comment">/// Shared function for the below tests, so that we test the same network in both cases.</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="comment"></span><a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> CreateTransposeTestNetwork()</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> {</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="comment">// Create a network</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</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="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keyword">auto</span> input = network->AddInputLayer(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  input->GetOutputSlot(0).SetTensorInfo(inputInfo);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="comment">// Insert Permute which swaps batches and channels dimensions</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keyword">auto</span> permute = network->AddTransposeLayer(<a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">TransposeDescriptor</a>(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>{ 3, 1, 2, 0 }), <span class="stringliteral">"permute"</span>);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> permuteInfo({ 4, 2, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  permute->GetOutputSlot(0).SetTensorInfo(permuteInfo);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  input->GetOutputSlot(0).Connect(permute->GetInputSlot(0));</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="comment">// Insert BatchToSpace</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">BatchToSpaceNdDescriptor</a> batchToSpaceDesc;</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = { 2, 2 };</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keyword">auto</span> batchToSpace = network->AddBatchToSpaceNdLayer(batchToSpaceDesc, <span class="stringliteral">"batchToSpace"</span>);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> batchToSpaceInfo({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  batchToSpace->GetOutputSlot(0).SetTensorInfo(batchToSpaceInfo);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  permute->GetOutputSlot(0).Connect(batchToSpace->GetInputSlot(0));</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> </div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keyword">auto</span> output = network->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  batchToSpace->GetOutputSlot(0).Connect(output->GetInputSlot(0));</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> }</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> <span class="comment"></span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> <span class="comment">/// Tests that a optimization performed by PermuteAndBatchToSpaceAsDepthToSpace does not change the behaviour</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> <span class="comment">/// of the network (i.e. it still produces the correct output).</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> <span class="comment"></span>TEST_CASE(<span class="stringliteral">"PermuteAndBatchToSpaceAsDepthToSpaceCorrectnessTest"</span>)</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="_cl_custom_allocator_tests_8cpp.xhtml#a15610ca027aa31bdc7f39133cd79359a">CreateTestNetwork</a>();</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>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime = <a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>());</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optimizedNetwork = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a> }, runtime->GetDeviceSpec());</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="comment">// Confirm that the optimization has actually taken place</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& optGraph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optimizedNetwork.get());</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  CHECK(<a class="code" href="est_utils_2_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(optGraph.cbegin(), optGraph.cend(), &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  &IsLayerOfType<DepthToSpaceLayer>, &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <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="l00225"></a><span class="lineno"> 225</span>  <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> netId;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  runtime->LoadNetwork(netId, std::move(optimizedNetwork));</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  std::vector<float> inputData{</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="comment">// Each row here is a row of pixels where each pixel has 4 channels</span></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="comment">// clang-format off</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  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="l00232"></a><span class="lineno"> 232</span>  -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="l00233"></a><span class="lineno"> 233</span>  <span class="comment">// clang-format on</span></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>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> input(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>), inputData);</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputs = { { 0, input } };</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  std::vector<float> outputData(4 * 6);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a> output(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), outputData.data());</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputs = { { 0, output } };</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  runtime->EnqueueWorkload(netId, inputs, outputs);</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>  <span class="comment">// Check the output is as expected.</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="comment">// Note this output has been generated by running the network *without* the optimization.</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  std::vector<float> expectedOutput = {</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <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="l00246"></a><span class="lineno"> 246</span>  <span class="comment">// clang-format off</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  1.0f, 2.0f, 10.0f, 20.0f, 100.0f, 200.0f,</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  3.0f, 4.0f, 30.0f, 40.0f, 300.0f, 400.0f,</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> </div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  -1.0f, -2.0f, -10.0f, -20.0f, -100.0f, -200.0f,</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  -3.0f, -4.0f, -30.0f, -40.0f, -300.0f, -400.0f,</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="comment">// clang-format on</span></div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  };</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  CHECK(outputData == expectedOutput);</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="comment"></span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> <span class="comment">/// Tests that a optimization performed by PermuteAndBatchToSpaceAsDepthToSpace does not change the behaviour</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> <span class="comment">/// of the network (i.e. it still produces the correct output).</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> <span class="comment"></span>TEST_CASE(<span class="stringliteral">"TransposeAndBatchToSpaceAsDepthToSpaceCorrectnessTest"</span>)</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> {</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = CreateTransposeTestNetwork();</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>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime = <a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>());</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optimizedNetwork = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a> }, runtime->GetDeviceSpec());</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> </div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="comment">// Confirm that the optimization has actually taken place</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& optGraph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optimizedNetwork.get());</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  CHECK(<a class="code" href="est_utils_2_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(optGraph.cbegin(), optGraph.cend(), &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  &IsLayerOfType<DepthToSpaceLayer>, &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <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="l00272"></a><span class="lineno"> 272</span>  <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> netId;</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  runtime->LoadNetwork(netId, std::move(optimizedNetwork));</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> </div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  std::vector<float> inputData{</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="comment">// Each row here is a row of pixels where each pixel has 4 channels</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="comment">// clang-format off</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  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="l00279"></a><span class="lineno"> 279</span>  -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="l00280"></a><span class="lineno"> 280</span>  <span class="comment">// clang-format on</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  };</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> input(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>), inputData);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputs = { { 0, input } };</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  std::vector<float> outputData(4 * 6);</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a> output(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), outputData.data());</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputs = { { 0, output } };</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  runtime->EnqueueWorkload(netId, inputs, outputs);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> </div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="comment">// Check the output is as expected.</span></div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="comment">// Note this output has been generated by running the network *without* the optimization.</span></div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  std::vector<float> expectedOutput = {</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <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="l00293"></a><span class="lineno"> 293</span>  <span class="comment">// clang-format off</span></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  1.0f, 2.0f, 10.0f, 20.0f, 100.0f, 200.0f,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  3.0f, 4.0f, 30.0f, 40.0f, 300.0f, 400.0f,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> </div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  -1.0f, -2.0f, -10.0f, -20.0f, -100.0f, -200.0f,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  -3.0f, -4.0f, -30.0f, -40.0f, -300.0f, -400.0f,</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="comment">// clang-format on</span></div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  };</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  CHECK(outputData == expectedOutput);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> }</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> <span class="preprocessor">#endif</span></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="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#l00049">Runtime.cpp:49</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a></div><div class="ttdoc">CPU Execution: Reference C++ kernels. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_aa7427025a851113a492de0b68b23d22a"><div class="ttname"><a href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">armnn::MakeOptimizations</a></div><div class="ttdeci">Optimizer::Optimizations MakeOptimizations(Args &&... args)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8hpp_source.xhtml#l00043">Optimizer.hpp:43</a></div></div> +<div class="ttc" id="est_utils_2_test_utils_8hpp_xhtml_a0eedb278f57355b47fa983450d4e378c"><div class="ttname"><a href="est_utils_2_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a></div><div class="ttdeci">bool CheckSequence(const armnn::Graph::ConstIterator first, const armnn::Graph::ConstIterator last)</div><div class="ttdef"><b>Definition:</b> <a href="est_utils_2_test_utils_8hpp_source.xhtml#l00021">TestUtils.hpp:21</a></div></div> +<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_a98f54d4391347d517c7a7869e7707203"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#a98f54d4391347d517c7a7869e7707203">armnn::optimizations::TransposeAndBatchToSpaceAsDepthToSpace</a></div><div class="ttdeci">OptimizeForConnection< TransposeLayer, BatchToSpaceNdLayer, PermuteAndBatchToSpaceAsDepthToSpaceImpl< TransposeLayer > > TransposeAndBatchToSpaceAsDepthToSpace</div><div class="ttdef"><b>Definition:</b> <a href="_permute_and_batch_to_space_as_depth_to_space_8hpp_source.xhtml#l00104">PermuteAndBatchToSpaceAsDepthToSpace.hpp:104</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="namespacearmnn_1_1optimizations_xhtml"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml">armnn::optimizations</a></div><div class="ttdef"><b>Definition:</b> <a href="_add_broadcast_reshape_layer_8hpp_source.xhtml#l00015">AddBroadcastReshapeLayer.hpp:15</a></div></div> +<div class="ttc" id="classarmnn_1_1_graph_xhtml_a98b1109a9006f8cc7d4566146a3bd737"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#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.xhtml#l00179">Graph.hpp:179</a></div></div> +<div class="ttc" id="classarmnn_1_1_optimizer_xhtml_a1f48ba622b76ea04d15c9b62f642bf08"><div class="ttname"><a href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a></div><div class="ttdeci">static void Pass(Graph &graph, const Optimizations &optimizations)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8cpp_source.xhtml#l00016">Optimizer.cpp:16</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#l00033">IRuntime.hpp:33</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#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.xhtml#l00863">Descriptors.hpp:863</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#l00392">Tensor.hpp:392</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a></div><div class="ttdoc">A BatchToSpaceNdDescriptor for the BatchToSpaceNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00836">Descriptors.hpp:836</a></div></div> +<div class="ttc" id="classarmnn_1_1_network_impl_xhtml"><div class="ttname"><a href="classarmnn_1_1_network_impl.xhtml">armnn::NetworkImpl</a></div><div class="ttdoc">Private implementation of INetwork. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8hpp_source.xhtml#l00031">Network.hpp:31</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#l00319">Tensor.hpp:319</a></div></div> +<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_a17d1279f5f8e3b92c328b1ed3b6fd549"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#a17d1279f5f8e3b92c328b1ed3b6fd549">armnn::optimizations::PermuteAndBatchToSpaceAsDepthToSpace</a></div><div class="ttdeci">OptimizeForConnection< PermuteLayer, BatchToSpaceNdLayer, PermuteAndBatchToSpaceAsDepthToSpaceImpl< PermuteLayer > > PermuteAndBatchToSpaceAsDepthToSpace</div><div class="ttdef"><b>Definition:</b> <a href="_permute_and_batch_to_space_as_depth_to_space_8hpp_source.xhtml#l00102">PermuteAndBatchToSpaceAsDepthToSpace.hpp:102</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#l01864">Network.cpp:1864</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml_a02e143524aefddd40b485fcf7dea6696"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">armnn::BatchToSpaceNdDescriptor::m_BlockShape</a></div><div class="ttdeci">std::vector< unsigned int > m_BlockShape</div><div class="ttdoc">Block shape values. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00859">Descriptors.hpp:859</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a0d8160388a127c1a23b37bc88dc6e2ec"><div class="ttname"><a href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00027">IRuntime.hpp:27</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#l00327">Tensor.hpp:327</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#l00393">Tensor.hpp:393</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#l00239">INetwork.hpp:239</a></div></div> +<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00295">Types.hpp:295</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#l00030">Graph.hpp:30</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#l00077">IRuntime.hpp:77</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#l00049">TestUtils.cpp:49</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_descriptor.xhtml">armnn::TransposeDescriptor</a></div><div class="ttdoc">A TransposeDescriptor for the TransposeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01467">Descriptors.hpp:1467</a></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_graph_xhtml_a02fd29b6dc3e21fbe4484362d85893bc"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#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.xhtml#l00181">Graph.hpp:181</a></div></div> +<div class="ttc" id="_cl_custom_allocator_tests_8cpp_xhtml_a15610ca027aa31bdc7f39133cd79359a"><div class="ttname"><a href="_cl_custom_allocator_tests_8cpp.xhtml#a15610ca027aa31bdc7f39133cd79359a">CreateTestNetwork</a></div><div class="ttdeci">armnn::INetworkPtr CreateTestNetwork(armnn::TensorInfo &inputTensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_cl_custom_allocator_tests_8cpp_source.xhtml#l00062">ClCustomAllocatorTests.cpp:62</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#l00238">INetwork.hpp:238</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#l00475">Network.cpp:475</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00217">Layer.hpp:217</a></div></div> +<div class="ttc" id="classarmnn_1_1_depth_to_space_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_depth_to_space_layer.xhtml">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.xhtml#l00014">DepthToSpaceLayer.hpp:14</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div> +<div class="ttc" id="structarmnn_1_1_permute_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_permute_descriptor.xhtml">armnn::PermuteDescriptor</a></div><div class="ttdoc">A PermuteDescriptor for the PermuteLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00129">Descriptors.hpp:129</a></div></div> +</div><!-- fragment --> +</div> +</div> +</div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_e0a84d05c80a2ef4231141dcbbeac5c8.xhtml">armnn</a></li><li class="navelem"><a class="el" href="dir_9d86fd1fbecbedf5bdb69c7e7235fe5f.xhtml">test</a></li><li class="navelem"><a class="el" href="dir_f1cd0e6da811a659c139424442adfb5f.xhtml">optimizations</a></li><li class="navelem"><a class="el" href="_permute_and_batch_to_space_as_depth_to_space_tests_8cpp.xhtml">PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp</a></li> + <li class="footer">Generated on Fri Aug 19 2022 14:38:31 for ArmNN by + <a href="http://www.doxygen.org/index.html"> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li> + </ul> +</div> +</body> +</html> |