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diff --git a/21.02/_fp32_network_to_bf16_converter_tests_8cpp.xhtml b/21.02/_fp32_network_to_bf16_converter_tests_8cpp.xhtml new file mode 100644 index 0000000000..925a99e6cb --- /dev/null +++ b/21.02/_fp32_network_to_bf16_converter_tests_8cpp.xhtml @@ -0,0 +1,257 @@ +<!-- 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/Fp32NetworkToBf16ConverterTests.cpp File Reference</title> +<link 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href="#func-members">Functions</a> </div> + <div class="headertitle"> +<div class="title">Fp32NetworkToBf16ConverterTests.cpp File Reference</div> </div> +</div><!--header--> +<div class="contents"> +<div class="textblock"><code>#include "<a class="el" href="_test_utils_8hpp_source.xhtml">../TestUtils.hpp</a>"</code><br /> +<code>#include <<a class="el" href="_optimizer_8hpp_source.xhtml">Optimizer.hpp</a>></code><br /> +<code>#include <boost/test/unit_test.hpp></code><br /> +</div> +<p><a href="_fp32_network_to_bf16_converter_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:a3c16690ed48a58d4b024de9c0cf490c9"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="_fp32_network_to_bf16_converter_tests_8cpp.xhtml#a3c16690ed48a58d4b024de9c0cf490c9">BOOST_AUTO_TEST_CASE</a> (Fp32NetworkToBf16OptimizationNoConversionTest)</td></tr> +<tr class="separator:a3c16690ed48a58d4b024de9c0cf490c9"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:a78c4780c103bf68961b505de64f20cdf"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="_fp32_network_to_bf16_converter_tests_8cpp.xhtml#a78c4780c103bf68961b505de64f20cdf">BOOST_AUTO_TEST_CASE</a> (Fp32NetworkToBf16OptimizationConv2DTest)</td></tr> +<tr class="separator:a78c4780c103bf68961b505de64f20cdf"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:ae3f365b5ca68aecf8ede5cf6ffe2ed31"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="_fp32_network_to_bf16_converter_tests_8cpp.xhtml#ae3f365b5ca68aecf8ede5cf6ffe2ed31">BOOST_AUTO_TEST_CASE</a> (Fp32NetworkToBf16OptimizationFullyConnectedTest)</td></tr> +<tr class="separator:ae3f365b5ca68aecf8ede5cf6ffe2ed31"><td class="memSeparator" colspan="2"> </td></tr> +</table> +<h2 class="groupheader">Function Documentation</h2> +<a id="a3c16690ed48a58d4b024de9c0cf490c9"></a> +<h2 class="memtitle"><span class="permalink"><a href="#a3c16690ed48a58d4b024de9c0cf490c9">◆ </a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[1/3]</span></h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname">BOOST_AUTO_TEST_CASE </td> + <td>(</td> + <td class="paramtype">Fp32NetworkToBf16OptimizationNoConversionTest </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="_fp32_network_to_bf16_converter_tests_8cpp_source.xhtml#l00015">15</a> of file <a class="el" href="_fp32_network_to_bf16_converter_tests_8cpp_source.xhtml">Fp32NetworkToBf16ConverterTests.cpp</a>.</p> + +<p class="reference">References <a class="el" href="_graph_8hpp_source.xhtml#l00402">Graph::AddLayer()</a>, <a class="el" href="_graph_8hpp_source.xhtml#l00172">Graph::cbegin()</a>, <a class="el" href="_graph_8hpp_source.xhtml#l00174">Graph::cend()</a>, <a class="el" href="_test_utils_8hpp_source.xhtml#l00021">CheckSequence()</a>, <a class="el" href="_layer_8cpp_source.xhtml#l00083">OutputSlot::Connect()</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="_layer_8hpp_source.xhtml#l00318">Layer::GetOutputSlot()</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#l00058">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>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a> graph;</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">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> infoFP32({ 2, 2, 1, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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">// Create the simple test network without Conv2D/FullyConnected.</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</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="l00023"></a><span class="lineno"> 23</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>(infoFP32);</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="keyword">auto</span> floor = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_floor_layer.xhtml">armnn::FloorLayer</a>>(<span class="stringliteral">"floor"</span>);</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  floor-><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>(infoFP32);</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div><div class="line"><a name="l00028"></a><span class="lineno"> 28</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="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="comment">// Connect up the layers</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</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>(floor->GetInputSlot(0));</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  floor->GetOutputSlot().Connect(output->GetInputSlot(0));</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>  BOOST_TEST(<a class="code" href="_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<armnn::InputLayer>,</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  &IsLayerOfType<armnn::FloorLayer>, &IsLayerOfType<armnn::OutputLayer>));</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">// Run the optimizer</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</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#aeb70f8fcf5180bdd5c94be7bb2f9d176">Fp32NetworkToBf16Converter</a>()));</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>  BOOST_TEST(<a class="code" href="_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<armnn::InputLayer>,</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  &IsLayerOfType<armnn::FloorLayer>,</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  &IsLayerOfType<armnn::OutputLayer>));</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> }</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="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#l00402">Graph.hpp:402</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#l00172">Graph.hpp:172</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#l00083">Layer.cpp:83</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_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="classarmnn_1_1_floor_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_floor_layer.xhtml">armnn::FloorLayer</a></div><div class="ttdoc">This layer represents a floor operation. </div><div class="ttdef"><b>Definition:</b> <a href="_floor_layer_8hpp_source.xhtml#l00013">FloorLayer.hpp:13</a></div></div> +<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00029">Graph.hpp:29</a></div></div> +<div class="ttc" id="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="_test_utils_8hpp_xhtml_a0eedb278f57355b47fa983450d4e378c"><div class="ttname"><a href="_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="_test_utils_8hpp_source.xhtml#l00021">TestUtils.hpp:21</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#l00058">Layer.cpp:58</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#l00318">Layer.hpp:318</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#l00174">Graph.hpp:174</a></div></div> +<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_aeb70f8fcf5180bdd5c94be7bb2f9d176"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#aeb70f8fcf5180bdd5c94be7bb2f9d176">armnn::optimizations::Fp32NetworkToBf16Converter</a></div><div class="ttdeci">OptimizeForType< Layer, ConvertFp32NetworkToBf16Impl > Fp32NetworkToBf16Converter</div><div class="ttdef"><b>Definition:</b> <a href="_convert_fp32_network_to_bf16_8hpp_source.xhtml#l00075">ConvertFp32NetworkToBf16.hpp:75</a></div></div> +</div><!-- fragment --> +</div> +</div> +<a id="a78c4780c103bf68961b505de64f20cdf"></a> +<h2 class="memtitle"><span class="permalink"><a href="#a78c4780c103bf68961b505de64f20cdf">◆ </a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[2/3]</span></h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname">BOOST_AUTO_TEST_CASE </td> + <td>(</td> + <td class="paramtype">Fp32NetworkToBf16OptimizationConv2DTest </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="_fp32_network_to_bf16_converter_tests_8cpp_source.xhtml#l00045">45</a> of file <a class="el" href="_fp32_network_to_bf16_converter_tests_8cpp_source.xhtml">Fp32NetworkToBf16ConverterTests.cpp</a>.</p> + +<p class="reference">References <a class="el" href="_graph_8hpp_source.xhtml#l00402">Graph::AddLayer()</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::BFloat16</a>, <a class="el" href="_graph_8hpp_source.xhtml#l00172">Graph::cbegin()</a>, <a class="el" href="_graph_8hpp_source.xhtml#l00174">Graph::cend()</a>, <a class="el" href="_test_utils_8hpp_source.xhtml#l00021">CheckSequence()</a>, <a class="el" href="_layer_8cpp_source.xhtml#l00083">OutputSlot::Connect()</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00194">TensorInfo::GetDataType()</a>, <a class="el" href="_layer_8hpp_source.xhtml#l00318">Layer::GetOutputSlot()</a>, <a class="el" href="_convolution2d_layer_8hpp_source.xhtml#l00020">Convolution2dLayer::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#l00058">OutputSlot::SetTensorInfo()</a>.</p> +<div class="fragment"><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>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a> graph;</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> infoFP32({ 2, 3, 8, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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">// Create const tensor fp32 data</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dims[] = { 4, 2, 1, 1 };</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  std::vector<float> floatWeights{ 0.0f, -1.0f,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  3.8f, <span class="comment">// 0x40733333 Round down</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  3.1055E+29f, <span class="comment">// 0x707ADC3C Round up</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  9.149516E-10f, <span class="comment">// 0x307B7FFF Round down</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  -3.8f, <span class="comment">// 0xC0733333 Round down</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  -3.1055E+29f, <span class="comment">// 0xF07ADC3C Round up</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  -9.149516E-10f <span class="comment">// 0xB07B7FFF Round down</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>  <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>), floatWeights);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="comment">// Create const bias fp32 data</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasDims[] {4};</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  std::vector<float> floatBias{ 1.0f, 2.0f, 3.0f, 4.0f };</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> bias(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(1, biasDims, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), floatBias);</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">// A network with Convolution2d layer</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</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="l00070"></a><span class="lineno"> 70</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>(infoFP32);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> </div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">auto</span> conv = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">armnn::Convolution2dLayer</a>>(descriptor, <span class="stringliteral">"conv2d"</span>);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  conv-><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a> = std::make_unique<armnn::ScopedCpuTensorHandle>(weights);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  conv->m_Bias = std::make_unique<armnn::ScopedCpuTensorHandle>(bias);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  conv->GetOutputSlot().SetTensorInfo(infoFP32);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</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="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="comment">// Connect up the layers</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</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>(conv->GetInputSlot(0));</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  conv->GetOutputSlot().Connect(output->GetInputSlot(0));</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>  BOOST_TEST(<a class="code" href="_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<armnn::InputLayer>,</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  &IsLayerOfType<armnn::Convolution2dLayer>, &IsLayerOfType<armnn::OutputLayer>));</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="comment">// Run the optimizer</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</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#aeb70f8fcf5180bdd5c94be7bb2f9d176">Fp32NetworkToBf16Converter</a>()));</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>  BOOST_TEST(<a class="code" href="_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<armnn::InputLayer>,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, &IsLayerOfType<armnn::Convolution2dLayer>,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  &IsLayerOfType<armnn::OutputLayer>));</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensor = conv->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo();</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensor = conv->GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  BOOST_TEST((conv->GetDataType() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>));</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  BOOST_TEST((conv->m_Weight->GetTensorInfo().GetDataType() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>));</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  BOOST_TEST((conv->m_Bias->GetTensorInfo().GetDataType() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>));</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  BOOST_TEST((inputTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>));</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  BOOST_TEST((outputTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>));</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="comment">// Check whether data matches expected Bf16 data</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>* data = conv->m_Weight->GetTensor<<a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>>();</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  BOOST_CHECK(data[0] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(0.0f));</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  BOOST_CHECK(data[1] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(-1.0f));</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  BOOST_CHECK(data[2] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(3.796875f)); <span class="comment">// 0x4073</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  BOOST_CHECK(data[3] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(3.1072295E29f)); <span class="comment">// 0x707B</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  BOOST_CHECK(data[4] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(9.131327E-10f)); <span class="comment">// 0x307B</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  BOOST_CHECK(data[5] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(-3.796875f)); <span class="comment">// 0xC073</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  BOOST_CHECK(data[6] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(-3.1072295E29f)); <span class="comment">// 0xF07B</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  BOOST_CHECK(data[7] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(-9.131327E-10f)); <span class="comment">// 0xB07B</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> }</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="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#l00402">Graph.hpp:402</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#l00172">Graph.hpp:172</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00408">Descriptors.hpp:408</a></div></div> +<div class="ttc" id="classarmnn_1_1_b_float16_xhtml"><div class="ttname"><a href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a></div><div class="ttdef"><b>Definition:</b> <a href="_b_float16_8hpp_source.xhtml#l00014">BFloat16.hpp:14</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#l00083">Layer.cpp:83</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_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="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00194">Tensor.hpp:194</a></div></div> +<div class="ttc" id="classarmnn_1_1_convolution2d_layer_xhtml_a2664044e28e69309ea08ef385fe53903"><div class="ttname"><a href="classarmnn_1_1_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">armnn::Convolution2dLayer::m_Weight</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Weight</div><div class="ttdoc">A unique pointer to store Weight values. </div><div class="ttdef"><b>Definition:</b> <a href="_convolution2d_layer_8hpp_source.xhtml#l00020">Convolution2dLayer.hpp:20</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00314">Tensor.hpp:314</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a></div></div> +<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00029">Graph.hpp:29</a></div></div> +<div class="ttc" id="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="_test_utils_8hpp_xhtml_a0eedb278f57355b47fa983450d4e378c"><div class="ttname"><a href="_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="_test_utils_8hpp_source.xhtml#l00021">TestUtils.hpp:21</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#l00058">Layer.cpp:58</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#l00318">Layer.hpp:318</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#l00174">Graph.hpp:174</a></div></div> +<div class="ttc" id="classarmnn_1_1_convolution2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_convolution2d_layer.xhtml">armnn::Convolution2dLayer</a></div><div class="ttdoc">This layer represents a convolution 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_convolution2d_layer_8hpp_source.xhtml#l00015">Convolution2dLayer.hpp:15</a></div></div> +<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_aeb70f8fcf5180bdd5c94be7bb2f9d176"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#aeb70f8fcf5180bdd5c94be7bb2f9d176">armnn::optimizations::Fp32NetworkToBf16Converter</a></div><div class="ttdeci">OptimizeForType< Layer, ConvertFp32NetworkToBf16Impl > Fp32NetworkToBf16Converter</div><div class="ttdef"><b>Definition:</b> <a href="_convert_fp32_network_to_bf16_8hpp_source.xhtml#l00075">ConvertFp32NetworkToBf16.hpp:75</a></div></div> +</div><!-- fragment --> +</div> +</div> +<a id="ae3f365b5ca68aecf8ede5cf6ffe2ed31"></a> +<h2 class="memtitle"><span class="permalink"><a href="#ae3f365b5ca68aecf8ede5cf6ffe2ed31">◆ </a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[3/3]</span></h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname">BOOST_AUTO_TEST_CASE </td> + <td>(</td> + <td class="paramtype">Fp32NetworkToBf16OptimizationFullyConnectedTest </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="_fp32_network_to_bf16_converter_tests_8cpp_source.xhtml#l00115">115</a> of file <a class="el" href="_fp32_network_to_bf16_converter_tests_8cpp_source.xhtml">Fp32NetworkToBf16ConverterTests.cpp</a>.</p> + +<p class="reference">References <a class="el" href="_graph_8hpp_source.xhtml#l00402">Graph::AddLayer()</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::BFloat16</a>, <a class="el" href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END()</a>, <a class="el" href="_graph_8hpp_source.xhtml#l00172">Graph::cbegin()</a>, <a class="el" href="_graph_8hpp_source.xhtml#l00174">Graph::cend()</a>, <a class="el" href="_test_utils_8hpp_source.xhtml#l00021">CheckSequence()</a>, <a class="el" href="_layer_8cpp_source.xhtml#l00083">OutputSlot::Connect()</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00194">TensorInfo::GetDataType()</a>, <a class="el" href="_layer_8hpp_source.xhtml#l00318">Layer::GetOutputSlot()</a>, <a class="el" href="_fully_connected_layer_8hpp_source.xhtml#l00019">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#l00058">OutputSlot::SetTensorInfo()</a>.</p> +<div class="fragment"><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> {</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a> graph;</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> infoFP32({ 2, 3, 8, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="comment">// Create const tensor fp32 data</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dims[] = { 4, 2, 1, 1 };</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  std::vector<float> floatWeights{ 0.0f, -1.0f,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  3.8f, <span class="comment">// 0x40733333 Round down</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  3.1055E+29f, <span class="comment">// 0x707ADC3C Round up</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  9.149516E-10f, <span class="comment">// 0x307B7FFF Round down</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  -3.8f, <span class="comment">// 0xC0733333 Round down</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  -3.1055E+29f, <span class="comment">// 0xF07ADC3C Round up</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  -9.149516E-10f <span class="comment">// 0xB07B7FFF Round down</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  };</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <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>), floatWeights);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="comment">// Create const bias fp32 data</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasDims[] {4};</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  std::vector<float> floatBias{ 1.0f, 2.0f, 3.0f, 4.0f };</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> bias(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(1, biasDims, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), floatBias);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="comment">// A network with FullyConnected layer</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</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="l00140"></a><span class="lineno"> 140</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>(infoFP32);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> descriptor;</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> </div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="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>>(descriptor, <span class="stringliteral">"fully"</span>);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  fc-><a class="code" href="classarmnn_1_1_fully_connected_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a> = std::make_unique<armnn::ScopedCpuTensorHandle>(weights);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  fc->m_Bias = std::make_unique<armnn::ScopedCpuTensorHandle>(bias);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  fc->GetOutputSlot().SetTensorInfo(infoFP32);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="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="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="comment">// Connect up the layers</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</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="l00153"></a><span class="lineno"> 153</span>  fc->GetOutputSlot().Connect(output->GetInputSlot(0));</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  BOOST_TEST(<a class="code" href="_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<armnn::InputLayer>,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  &IsLayerOfType<armnn::FullyConnectedLayer>, &IsLayerOfType<armnn::OutputLayer>));</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">// Run the optimizer</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</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#aeb70f8fcf5180bdd5c94be7bb2f9d176">Fp32NetworkToBf16Converter</a>()));</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  BOOST_TEST(<a class="code" href="_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<armnn::InputLayer>,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, &IsLayerOfType<armnn::FullyConnectedLayer>,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  &IsLayerOfType<armnn::OutputLayer>));</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensor = fc->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo();</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensor = fc->GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  BOOST_TEST((fc->GetDataType() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>));</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  BOOST_TEST((fc->m_Weight->GetTensorInfo().GetDataType() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>));</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  BOOST_TEST((fc->m_Bias->GetTensorInfo().GetDataType() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>));</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  BOOST_TEST((inputTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>));</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  BOOST_TEST((outputTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>));</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="comment">// Check whether data matches expected Bf16 data</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>* data = fc->m_Weight->GetTensor<<a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>>();</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  BOOST_CHECK(data[0] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(0.0f));</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  BOOST_CHECK(data[1] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(-1.0f));</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  BOOST_CHECK(data[2] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(3.796875f)); <span class="comment">// 0x4073</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  BOOST_CHECK(data[3] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(3.1072295E29f)); <span class="comment">// 0x707B</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  BOOST_CHECK(data[4] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(9.131327E-10f)); <span class="comment">// 0x307B</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  BOOST_CHECK(data[5] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(-3.796875f)); <span class="comment">// 0xC073</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  BOOST_CHECK(data[6] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(-3.1072295E29f)); <span class="comment">// 0xF07B</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  BOOST_CHECK(data[7] == <a class="code" href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a>(-9.131327E-10f)); <span class="comment">// 0xB07B</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> }</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_fully_connected_layer_xhtml_a2664044e28e69309ea08ef385fe53903"><div class="ttname"><a href="classarmnn_1_1_fully_connected_layer.xhtml#a2664044e28e69309ea08ef385fe53903">armnn::FullyConnectedLayer::m_Weight</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > 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#l00019">FullyConnectedLayer.hpp:19</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="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#l00402">Graph.hpp:402</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#l00172">Graph.hpp:172</a></div></div> +<div class="ttc" id="classarmnn_1_1_b_float16_xhtml"><div class="ttname"><a href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a></div><div class="ttdef"><b>Definition:</b> <a href="_b_float16_8hpp_source.xhtml#l00014">BFloat16.hpp:14</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#l00083">Layer.cpp:83</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_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="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_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00194">Tensor.hpp:194</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#l00389">Descriptors.hpp:389</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00314">Tensor.hpp:314</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a></div></div> +<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00029">Graph.hpp:29</a></div></div> +<div class="ttc" id="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="_test_utils_8hpp_xhtml_a0eedb278f57355b47fa983450d4e378c"><div class="ttname"><a href="_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="_test_utils_8hpp_source.xhtml#l00021">TestUtils.hpp:21</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#l00058">Layer.cpp:58</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#l00318">Layer.hpp:318</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#l00174">Graph.hpp:174</a></div></div> +<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_aeb70f8fcf5180bdd5c94be7bb2f9d176"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#aeb70f8fcf5180bdd5c94be7bb2f9d176">armnn::optimizations::Fp32NetworkToBf16Converter</a></div><div class="ttdeci">OptimizeForType< Layer, ConvertFp32NetworkToBf16Impl > Fp32NetworkToBf16Converter</div><div class="ttdef"><b>Definition:</b> <a href="_convert_fp32_network_to_bf16_8hpp_source.xhtml#l00075">ConvertFp32NetworkToBf16.hpp:75</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="_fp32_network_to_bf16_converter_tests_8cpp.xhtml">Fp32NetworkToBf16ConverterTests.cpp</a></li> + <li class="footer">Generated on Thu Feb 25 2021 17:27:54 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> |