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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/_convert_constants_float_to_half_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/_convert_constants_float_to_half_tests_8cpp.xhtml')
| -rw-r--r-- | 22.08/_convert_constants_float_to_half_tests_8cpp.xhtml | 175 |
1 files changed, 175 insertions, 0 deletions
diff --git a/22.08/_convert_constants_float_to_half_tests_8cpp.xhtml b/22.08/_convert_constants_float_to_half_tests_8cpp.xhtml new file mode 100644 index 0000000000..4d890b8856 --- /dev/null +++ b/22.08/_convert_constants_float_to_half_tests_8cpp.xhtml @@ -0,0 +1,175 @@ +<!-- 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/ConvertConstantsFloatToHalfTests.cpp File Reference</title> +<link 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href="#func-members">Functions</a> </div> + <div class="headertitle"> +<div class="title">ConvertConstantsFloatToHalfTests.cpp File Reference</div> </div> +</div><!--header--> +<div class="contents"> +<div class="textblock"><code>#include <TestUtils.hpp></code><br /> +<code>#include <<a class="el" href="_optimizer_8hpp_source.xhtml">Optimizer.hpp</a>></code><br /> +<code>#include <<a class="el" href="_half_8hpp_source.xhtml">Half.hpp</a>></code><br /> +<code>#include <doctest/doctest.h></code><br /> +</div> +<p><a href="_convert_constants_float_to_half_tests_8cpp_source.xhtml">Go to the source code of this file.</a></p> +<table class="memberdecls"> +<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a> +Functions</h2></td></tr> +<tr class="memitem:a77a062dba8ec73047ae4e734519f5ef8"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="_convert_constants_float_to_half_tests_8cpp.xhtml#a77a062dba8ec73047ae4e734519f5ef8">TEST_SUITE</a> ("Optimizer")</td></tr> +<tr class="separator:a77a062dba8ec73047ae4e734519f5ef8"><td class="memSeparator" colspan="2"> </td></tr> +</table> +<h2 class="groupheader">Function Documentation</h2> +<a id="a77a062dba8ec73047ae4e734519f5ef8"></a> +<h2 class="memtitle"><span class="permalink"><a href="#a77a062dba8ec73047ae4e734519f5ef8">◆ </a></span>TEST_SUITE()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname">TEST_SUITE </td> + <td>(</td> + <td class="paramtype">"Optimizer" </td> + <td class="paramname"></td><td>)</td> + <td></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_convert_constants_float_to_half_tests_8cpp_source.xhtml#l00015">15</a> of file <a class="el" href="_convert_constants_float_to_half_tests_8cpp_source.xhtml">ConvertConstantsFloatToHalfTests.cpp</a>.</p> + +<p class="reference">References <a class="el" href="_graph_8hpp_source.xhtml#l00456">Graph::AddLayer()</a>, <a class="el" href="_layer_8cpp_source.xhtml#l00112">OutputSlot::Connect()</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::Float16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="_layer_8cpp_source.xhtml#l00313">Layer::GetDataType()</a>, <a class="el" href="_layer_8hpp_source.xhtml#l00324">Layer::GetInputSlot()</a>, <a class="el" href="_layer_8hpp_source.xhtml#l00326">Layer::GetOutputSlot()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00487">FullyConnectedDescriptor::m_BiasEnabled</a>, <a class="el" href="_constant_layer_8hpp_source.xhtml#l00044">ConstantLayer::m_LayerOutput</a>, <a class="el" href="_fully_connected_layer_8hpp_source.xhtml#l00020">FullyConnectedLayer::m_Weight</a>, <a class="el" href="_optimizer_8hpp_source.xhtml#l00043">armnn::MakeOptimizations()</a>, <a class="el" href="_optimizer_8cpp_source.xhtml#l00016">Optimizer::Pass()</a>, and <a class="el" href="_layer_8cpp_source.xhtml#l00087">OutputSlot::SetTensorInfo()</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> TEST_CASE(<span class="stringliteral">"ConvertConstantsFloatToHalfTest"</span>)</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> {</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a> graph;</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 1, 1, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>);</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>  <span class="comment">// Create const tensor from fp32 data</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dims[] = { 4, 1, 1, 1 };</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  std::vector<float> floatWeights{ 1.0f, 2.0f, 3.0f, 4.0f };</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, dims, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>), floatWeights);</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="comment">// Create simple test network</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keyword">auto</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">armnn::InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(info);</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keyword">auto</span> fc = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_fully_connected_layer.xhtml">armnn::FullyConnectedLayer</a>>(<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a>(), <span class="stringliteral">"fc"</span>);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  fc-><a class="code" href="classarmnn_1_1_fully_connected_layer.xhtml#a6266a703017d7296f87cc4923df2d725">m_Weight</a> = std::make_unique<armnn::ScopedTensorHandle>(weights);</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  fc->GetOutputSlot().SetTensorInfo(info);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keyword">auto</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">armnn::OutputLayer</a>>(1, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="comment">// Connect up the layers</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(fc->GetInputSlot(0));</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  fc->GetOutputSlot().Connect(output->GetInputSlot(0));</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="comment">// Check tensor data type before conversion</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="comment">// Run the optimizer</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">armnn::MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a226cef3d775179e25ee35d231f4e8fae">ConvertConstantsFloatToHalf</a>()));</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="comment">// Check tensor data type after conversion</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="comment">// Check whether data matches expected fp16 data</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>* data = fc->m_Weight->GetConstTensor<<a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>>();</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  CHECK(data[0] == <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>(1.0f));</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  CHECK(data[1] == <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>(2.0f));</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  CHECK(data[2] == <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>(3.0f));</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  CHECK(data[3] == <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>(4.0f));</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> }</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </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> TEST_CASE(<span class="stringliteral">"ConvertConstantsFloatToHalfTest_constant"</span>)</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a> graph;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="comment">// Create the simple test network with Weights and Biases as inputs to a FullyConnected layer.</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keyword">auto</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">armnn::InputLayer</a>>(0, <span class="stringliteral">"Input"</span>);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keyword">auto</span> weights = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_constant_layer.xhtml">armnn::ConstantLayer</a>>(<span class="stringliteral">"Weights"</span>);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">auto</span> biases = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_constant_layer.xhtml">armnn::ConstantLayer</a>>(<span class="stringliteral">"Biases"</span>);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> desc;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  desc.m_ConstantWeights = <span class="keyword">true</span>;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">auto</span> fcLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_fully_connected_layer.xhtml">armnn::FullyConnectedLayer</a>>(desc, <span class="stringliteral">"FullyConnected"</span>);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">auto</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">armnn::OutputLayer</a>>(1, <span class="stringliteral">"Output"</span>);</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="keywordtype">float</span> expectedWeightsData[] = { 1.0f, 2.0f, 3.0f, 4.0f };</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordtype">float</span> expectedBiasesData[] = { 2.0f, 2.0f };</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo ({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="comment">// Set the m_LayerOutput for the optimizer to point to.</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weightsTensor(weightsInfo, &expectedWeightsData);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biasesTensor(biasesInfo, &expectedBiasesData);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  weights->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(weightsTensor);</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  biases-><a class="code" href="classarmnn_1_1_constant_layer.xhtml#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a> = std::make_unique<armnn::ScopedTensorHandle>(biasesTensor);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  input->GetOutputSlot().SetTensorInfo(inputInfo);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  weights-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(weightsInfo);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  biases-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(biasesInfo);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  fcLayer->GetOutputSlot().SetTensorInfo(outputInfo);</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">// Connect up the layers</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  input->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(0));</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  weights-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(fcLayer->GetInputSlot(1));</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  biases-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(fcLayer->GetInputSlot(2));</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  fcLayer->GetOutputSlot(0).Connect(output-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="comment">// Check tensor data type before conversion</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  CHECK(weights->m_LayerOutput->GetTensorInfo().<a class="code" href="classarmnn_1_1_layer.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="comment">// Run the optimizer</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">armnn::MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a226cef3d775179e25ee35d231f4e8fae">ConvertConstantsFloatToHalf</a>()));</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="comment">// Check tensor data type after conversion</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  CHECK(weights->m_LayerOutput->GetTensorInfo().<a class="code" href="classarmnn_1_1_layer.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="comment">// Check whether weights data matches expected fp16 data</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>* data = weights->m_LayerOutput->GetConstTensor<<a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>>();</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  CHECK(data[0] == <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>(1.0f));</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  CHECK(data[1] == <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>(2.0f));</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  CHECK(data[2] == <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>(3.0f));</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  CHECK(data[3] == <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>(4.0f));</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="comment">// Check whether bias data matches expected fp16 data</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>* biasData = biases-><a class="code" href="classarmnn_1_1_constant_layer.xhtml#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a>->GetConstTensor<<a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>>();</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  CHECK(biasData[0] == <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>(2.0f));</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  CHECK(biasData[1] == <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>(2.0f));</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> }</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> </div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> }</div><div class="ttc" id="classarmnn_1_1_constant_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_constant_layer.xhtml">armnn::ConstantLayer</a></div><div class="ttdoc">A layer that the constant data can be bound to. </div><div class="ttdef"><b>Definition:</b> <a href="_constant_layer_8hpp_source.xhtml#l00015">ConstantLayer.hpp:15</a></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="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_constant_layer_xhtml_ad0c4b8ee0efd8f9336571cbeab8a53fe"><div class="ttname"><a href="classarmnn_1_1_constant_layer.xhtml#ad0c4b8ee0efd8f9336571cbeab8a53fe">armnn::ConstantLayer::m_LayerOutput</a></div><div class="ttdeci">std::shared_ptr< ConstTensorHandle > m_LayerOutput</div><div class="ttdef"><b>Definition:</b> <a href="_constant_layer_8hpp_source.xhtml#l00044">ConstantLayer.hpp:44</a></div></div> +<div class="ttc" id="classarmnn_1_1_graph_xhtml_a7563c5b899e7d0ada08fd0fdb202f205"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">armnn::Graph::AddLayer</a></div><div class="ttdeci">LayerT * AddLayer(Args &&... args)</div><div class="ttdoc">Adds a new layer, of type LayerType, to the graph constructed with the arguments passed. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00456">Graph.hpp:456</a></div></div> +<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_adcfb97035799ea4c043f9ef370714815"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">armnn::OutputSlot::Connect</a></div><div class="ttdeci">int Connect(InputSlot &destination)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00112">Layer.cpp:112</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="classarmnn_1_1_layer_xhtml_acf8b8e23bf647836592982f97088d375"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">armnn::Layer::GetInputSlot</a></div><div class="ttdeci">const InputSlot & GetInputSlot(unsigned int index) const override</div><div class="ttdoc">Get a const input slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00324">Layer.hpp:324</a></div></div> +<div class="ttc" id="classarmnn_1_1_output_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_output_layer.xhtml">armnn::OutputLayer</a></div><div class="ttdoc">A layer user-provided data can be bound to (e.g. inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_output_layer_8hpp_source.xhtml#l00013">OutputLayer.hpp:13</a></div></div> +<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_a226cef3d775179e25ee35d231f4e8fae"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#a226cef3d775179e25ee35d231f4e8fae">armnn::optimizations::ConvertConstantsFloatToHalf</a></div><div class="ttdeci">ConvertConstants< Float32ToFloat16, IsFloat16Layer > ConvertConstantsFloatToHalf</div><div class="ttdef"><b>Definition:</b> <a href="_convert_constants_8hpp_source.xhtml#l00153">ConvertConstants.hpp:153</a></div></div> +<div class="ttc" id="classarmnn_1_1_fully_connected_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_fully_connected_layer.xhtml">armnn::FullyConnectedLayer</a></div><div class="ttdoc">This layer represents a fully connected operation. </div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_layer_8hpp_source.xhtml#l00015">FullyConnectedLayer.hpp:15</a></div></div> +<div class="ttc" id="classarmnn_1_1_fully_connected_layer_xhtml_a6266a703017d7296f87cc4923df2d725"><div class="ttname"><a href="classarmnn_1_1_fully_connected_layer.xhtml#a6266a703017d7296f87cc4923df2d725">armnn::FullyConnectedLayer::m_Weight</a></div><div class="ttdeci">std::shared_ptr< ConstTensorHandle > m_Weight</div><div class="ttdoc">A unique pointer to store Weight values. </div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_layer_8hpp_source.xhtml#l00020">FullyConnectedLayer.hpp:20</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a></div><div class="ttdoc">A FullyConnectedDescriptor for the FullyConnectedLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00468">Descriptors.hpp:468</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::FullyConnectedDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00487">Descriptors.hpp:487</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_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</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="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div> +<div class="ttc" id="classarmnn_1_1_input_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_input_layer.xhtml">armnn::InputLayer</a></div><div class="ttdoc">A layer user-provided data can be bound to (e.g. inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_input_layer_8hpp_source.xhtml#l00013">InputLayer.hpp:13</a></div></div> +<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_a7e5c5771d741dd5473989047a9314728"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">armnn::OutputSlot::SetTensorInfo</a></div><div class="ttdeci">void SetTensorInfo(const TensorInfo &tensorInfo) override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00087">Layer.cpp:87</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#aea909c7327109228ef618d459015def3">armnn::Layer::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00313">Layer.cpp:313</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml_a0e36688a43c35668d8db5257274c68fe"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">armnn::Layer::GetOutputSlot</a></div><div class="ttdeci">const OutputSlot & GetOutputSlot(unsigned int index=0) const override</div><div class="ttdoc">Get the const output slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00326">Layer.hpp:326</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="namespacearmnn_xhtml_a0f38fa92b2468d5378258a2b074c1a31"><div class="ttname"><a href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">armnn::Half</a></div><div class="ttdeci">half_float::half Half</div><div class="ttdef"><b>Definition:</b> <a href="_half_8hpp_source.xhtml#l00018">Half.hpp:18</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="_convert_constants_float_to_half_tests_8cpp.xhtml">ConvertConstantsFloatToHalfTests.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" 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