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<div class="title">CaffeParser.cpp</div>  </div>
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<a href="_caffe_parser_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_caffe_parser_8hpp.xhtml">CaffeParser.hpp</a>&quot;</span></div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_record_by_record_caffe_parser_8hpp.xhtml">RecordByRecordCaffeParser.hpp</a>&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="include_2armnn_caffe_parser_2_version_8hpp.xhtml">armnnCaffeParser/Version.hpp</a>&quot;</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;</div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>&quot;</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>&quot;</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_utils_8hpp.xhtml">armnn/Utils.hpp</a>&quot;</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_exceptions_8hpp.xhtml">armnn/Exceptions.hpp</a>&quot;</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;</div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_graph_topological_sort_8hpp.xhtml">GraphTopologicalSort.hpp</a>&quot;</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_verification_helpers_8hpp.xhtml">VerificationHelpers.hpp</a>&quot;</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;</div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_assert_8hpp.xhtml">armnn/utility/Assert.hpp</a>&gt;</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_numeric_cast_8hpp.xhtml">armnn/utility/NumericCast.hpp</a>&gt;</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;</div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="preprocessor">#include &lt;fmt/format.h&gt;</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;</div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment">// Caffe</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="preprocessor">#include &quot;caffe/proto/caffe.pb.h&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="comment">// ProtoBuf</span></div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">#include &lt;google/protobuf/io/coded_stream.h&gt;</span></div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="preprocessor">#include &lt;google/protobuf/io/zero_copy_stream.h&gt;</span></div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;<span class="preprocessor">#include &lt;google/protobuf/io/zero_copy_stream_impl.h&gt;</span></div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="preprocessor">#include &lt;google/protobuf/text_format.h&gt;</span></div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="preprocessor">#include &lt;google/protobuf/stubs/common.h&gt;</span></div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="preprocessor">#include &lt;google/protobuf/stubs/once.h&gt;</span></div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="preprocessor">#include &lt;google/protobuf/io/coded_stream.h&gt;</span></div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="preprocessor">#include &lt;google/protobuf/descriptor.h&gt;</span></div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;<span class="preprocessor">#include &lt;google/protobuf/generated_message_reflection.h&gt;</span></div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="preprocessor">#include &lt;google/protobuf/reflection_ops.h&gt;</span></div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="preprocessor">#include &lt;google/protobuf/wire_format.h&gt;</span></div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;</div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="preprocessor">#include &lt;cmath&gt;</span></div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="preprocessor">#include &lt;iostream&gt;</span></div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#include &lt;sstream&gt;</span></div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#include &lt;queue&gt;</span></div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="preprocessor">#include &lt;fcntl.h&gt;</span></div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="comment">/// Caffe networks are loaded from protobuf files (binary or text) using the protobuf library and the generated</span></div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="comment">/// code from caffe.pb.h. This gives us a caffe::NetParameter which is an in-memory version of the file.</span></div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="comment">/// This contains a flat list of Caffe &#39;layers&#39; (e.g. convolution, pooling etc.).</span></div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="comment">/// Each layer has inputs (called &quot;bottoms&quot;) and outputs (called &quot;tops&quot;). Data flows from bottom to top.</span></div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="comment">/// The bottoms of a layer refer to the tops of other layers, not their names.</span></div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="comment">/// The names of layers seem to be arbitrary (you could rename a layer and the network wouldn&#39;t</span></div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;<span class="comment">/// need any other changes).</span></div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;<span class="comment">///</span></div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;<span class="comment">/// Some layers (e.g. Relu) can be configured so that their top and bottom are both the same. This is called an</span></div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;<span class="comment">/// &quot;in-place&quot; layer and is a Caffe runtime feature used to reduce memory usage by modifying tensors in-place.</span></div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;<span class="comment">/// This isn&#39;t relevant to the parser and so we preprocess these layers to convert them to regular layers, to result</span></div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;<span class="comment">/// in a consistent graph structure.</span></div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn_caffe_parser.xhtml">armnnCaffeParser</a></div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;{</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacecaffe.xhtml">caffe</a>;</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacestd.xhtml">std</a>;</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacegoogle_1_1protobuf_1_1io.xhtml">google::protobuf::io</a>;</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;ICaffeParser::ICaffeParser() : pCaffeParserImpl(new RecordByRecordCaffeParser()) {}</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;ICaffeParser::~ICaffeParser() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;</div><div class="line"><a name="l00070"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#a706b8481b6bd660dd3c3898fdf7a2993">   70</a></span>&#160;<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml">ICaffeParser</a>* ICaffeParser::CreateRaw()</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;{</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    <span class="keywordflow">return</span> <span class="keyword">new</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml">ICaffeParser</a>();</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;}</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;</div><div class="line"><a name="l00075"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#abd42446e41480b0cc9df7ce06af412e3">   75</a></span>&#160;<a class="code" href="namespacearmnn_caffe_parser.xhtml#a33c76910f1980ffaa41c22e0151cce2a">ICaffeParserPtr</a> ICaffeParser::Create()</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;{</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_caffe_parser.xhtml#a33c76910f1980ffaa41c22e0151cce2a">ICaffeParserPtr</a>(CreateRaw(), &amp;ICaffeParser::Destroy);</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;}</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;</div><div class="line"><a name="l00080"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#a5e8137c09390352d2f8b420d147d3b2e">   80</a></span>&#160;<span class="keywordtype">void</span> ICaffeParser::Destroy(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml">ICaffeParser</a>* parser)</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;{</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    <span class="keyword">delete</span> parser;</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;}</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#add49602ee9cd2bd16c1c4ccd25555d8e">   85</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> ICaffeParser::CreateNetworkFromTextFile(</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    <span class="keyword">const</span> std::map&lt;std::string, armnn::TensorShape&gt;&amp; inputShapes,</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;    <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; requestedOutputs)</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;{</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    <span class="keywordflow">return</span> pCaffeParserImpl-&gt;<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#add49602ee9cd2bd16c1c4ccd25555d8e">CreateNetworkFromTextFile</a>(graphFile, inputShapes, requestedOutputs);</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;}</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;</div><div class="line"><a name="l00093"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#a56e7dd134fad3b70cb926b447fe2d16e">   93</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> ICaffeParser::CreateNetworkFromBinaryFile(</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    <span class="keyword">const</span> std::map&lt;std::string, armnn::TensorShape&gt;&amp; inputShapes,</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;    <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; requestedOutputs)</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;{</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    <span class="keywordflow">return</span> pCaffeParserImpl-&gt;CreateNetworkFromBinaryFile(graphFile, inputShapes,requestedOutputs);</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;}</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;</div><div class="line"><a name="l00101"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#a5448fd503a576d279c71aa8340e84b7f">  101</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> ICaffeParser::CreateNetworkFromString(</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">char</span>* protoText,</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    <span class="keyword">const</span> std::map&lt;std::string, armnn::TensorShape&gt;&amp; inputShapes,</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; requestedOutputs)</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;{</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    <span class="keywordflow">return</span> pCaffeParserImpl-&gt;CreateNetworkFromString(protoText, inputShapes, requestedOutputs);</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;}</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;</div><div class="line"><a name="l00109"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#a8b053a6c449d0814cc831c916c126668">  109</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">BindingPointInfo</a> ICaffeParser::GetNetworkInputBindingInfo(<span class="keyword">const</span> std::string&amp; name)<span class="keyword"> const</span></div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    <span class="keywordflow">return</span> pCaffeParserImpl-&gt;GetNetworkInputBindingInfo(name);</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;}</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;</div><div class="line"><a name="l00114"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#a4b1fdcb1985af12dd1848a9ffa5d3271">  114</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">BindingPointInfo</a> ICaffeParser::GetNetworkOutputBindingInfo(<span class="keyword">const</span> std::string&amp; name)<span class="keyword"> const</span></div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <span class="keywordflow">return</span> pCaffeParserImpl-&gt;GetNetworkOutputBindingInfo(name);</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;}</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;{</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;<span class="keyword">const</span> <span class="keywordtype">float</span>* GetArrayPtrFromBlob(<span class="keyword">const</span> LayerParameter&amp; layerParam, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> blobIndex)</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;{</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    <span class="keyword">auto</span> nBlobs = layerParam.blobs_size();</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    <span class="keywordflow">if</span> (blobIndex &gt;= armnn::numeric_cast&lt;unsigned int&gt;(nBlobs))</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    {</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;            fmt::format(<span class="stringliteral">&quot;Expected data blob at index {} in layer {} not found. nBlobs={}. {}&quot;</span>,</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;                        blobIndex,</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;                        nBlobs,</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    }</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;    <span class="keyword">const</span> BlobProto&amp; blob = layerParam.blobs(armnn::numeric_cast&lt;int&gt;(blobIndex));</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span>* arrayPtr = blob.data().data();</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    <span class="keywordflow">return</span> arrayPtr;</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;}</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;<span class="keywordtype">void</span> GetDataFromBlob(<span class="keyword">const</span> LayerParameter&amp; layerParam, vector&lt;float&gt;&amp; outData, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> blobIndex)</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;{</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    <span class="keyword">auto</span> nBlobs = layerParam.blobs_size();</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    <span class="keywordflow">if</span> (blobIndex &gt;= armnn::numeric_cast&lt;unsigned int&gt;(nBlobs))</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    {</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;            fmt::format(<span class="stringliteral">&quot;Expected data blob at index {} in layer {} not found. {}&quot;</span>,</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;                        blobIndex,</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;    }</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    <span class="keyword">const</span> BlobProto&amp; blob = layerParam.blobs(armnn::numeric_cast&lt;int&gt;(blobIndex));</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    <span class="keywordtype">size_t</span> blobSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a>&lt;<span class="keywordtype">size_t</span>&gt;(blob.data_size());</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    <span class="keywordflow">if</span> (blobSize != outData.size())</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    {</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;            fmt::format(<span class="stringliteral">&quot;Data blob at index {} in layer {} has an unexpected size. &quot;</span></div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;                        <span class="stringliteral">&quot;Expected {} elements but got {} elements. {}&quot;</span>,</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;                        blobIndex,</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;                        outData.size(),</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;                        blobSize,</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    }</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;    <span class="keywordtype">int</span> outSizeInt = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a>&lt;<span class="keywordtype">int</span>&gt;(outData.size());</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; outSizeInt; ++i)</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;    {</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;        outData[<span class="keyword">static_cast&lt;</span><span class="keywordtype">size_t</span><span class="keyword">&gt;</span>(i)] = blob.data(i);</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    }</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;}</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;<span class="keywordtype">size_t</span> SizeOfVectorData(<span class="keyword">const</span> vector&lt;T&gt;&amp; vec)</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;{</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    <span class="keywordflow">return</span> vec.size() * <span class="keyword">sizeof</span>(T);</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;}</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;<span class="keywordtype">void</span> ValidateNumInputsOutputs(<span class="keyword">const</span> caffe::LayerParameter&amp; layerParameter,</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;                              <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>                 numInputs,</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;                              <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>                 numOutputs)</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;{</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    <span class="keywordtype">int</span> numInputsActual = layerParameter.bottom_size();</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;    <span class="keywordflow">if</span> (numInputs != armnn::numeric_cast&lt;unsigned int&gt;(numInputsActual))</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;    {</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;            fmt::format(<span class="stringliteral">&quot;Invalid number of inputs requested {} for layer {} &quot;</span></div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;                        <span class="stringliteral">&quot;while only {} present. {}&quot;</span>,</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;                        numInputs,</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;                        layerParameter.name(),</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;                        numInputsActual,</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    }</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;    <span class="keywordtype">int</span> numOutputsActual = layerParameter.top_size();</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;    <span class="keywordflow">if</span> (numOutputs != armnn::numeric_cast&lt;unsigned int&gt;(numOutputsActual))</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;    {</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;            fmt::format(<span class="stringliteral">&quot;Invalid number of outputs requested {} for layer {} &quot;</span></div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;                        <span class="stringliteral">&quot;while only {} present. {}&quot;</span>,</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;                        numOutputs,</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;                        layerParameter.name(),</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;                        numOutputsActual,</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    }</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;}</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> ParamType, <span class="keyword">typename</span> ExtractOptional, <span class="keyword">typename</span> ExtractFallback, <span class="keyword">typename</span> ValueType&gt;</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;ValueType GetOptionalWithFallback(<span class="keyword">const</span> ParamType&amp; param,</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;                                  ExtractOptional extractOptional,</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;                                  ExtractFallback extractFallback,</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;                                  ValueType defaultValue)</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;{</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    <span class="keyword">auto</span> optValue = extractOptional(param, defaultValue);</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    <span class="keywordflow">if</span> (optValue.first)</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    {</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;        <span class="keywordflow">return</span> optValue.second;</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;    }</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;    <span class="keyword">auto</span> fallbackValue = extractFallback(param, defaultValue);</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;    <span class="keywordflow">return</span> fallbackValue.second;</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;}</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;</div><div class="line"><a name="l00225"></a><span class="lineno"><a class="line" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">  225</a></span>&#160;<span class="preprocessor">#define GET_OPTIONAL_WITH_VECTOR_FALLBACK(PARAM, \</span></div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;<span class="preprocessor">                                          PARAM_TYPE, \</span></div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;<span class="preprocessor">                                          OPTIONAL_VALUE, \</span></div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;<span class="preprocessor">                                          FALLBACK_VECTOR, \</span></div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;<span class="preprocessor">                                          VALUE_TYPE, \</span></div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;<span class="preprocessor">                                          DEFAULT_VALUE) \</span></div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;<span class="preprocessor">    GetOptionalWithFallback( \</span></div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;<span class="preprocessor">        PARAM, \</span></div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;<span class="preprocessor">        [](const PARAM_TYPE &amp; param, VALUE_TYPE defaultValue) \</span></div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;<span class="preprocessor">        { \</span></div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;<span class="preprocessor">            if (param.has_##OPTIONAL_VALUE ()) \</span></div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;<span class="preprocessor">            { \</span></div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;<span class="preprocessor">                return std::make_pair(true, param.OPTIONAL_VALUE ()); \</span></div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;<span class="preprocessor">            } \</span></div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;<span class="preprocessor">            else \</span></div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;<span class="preprocessor">            { \</span></div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;<span class="preprocessor">                return std::make_pair(false, defaultValue); \</span></div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;<span class="preprocessor">            } \</span></div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;<span class="preprocessor">        }, \</span></div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;<span class="preprocessor">        [](const PARAM_TYPE &amp; param, VALUE_TYPE defaultValue) \</span></div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;<span class="preprocessor">        { \</span></div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;<span class="preprocessor">            if (param.FALLBACK_VECTOR##_size() &gt; 0) \</span></div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;<span class="preprocessor">            { \</span></div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;<span class="preprocessor">                return std::make_pair(true, (param.FALLBACK_VECTOR ()).Get(0)); \</span></div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;<span class="preprocessor">            } \</span></div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;<span class="preprocessor">            else \</span></div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;<span class="preprocessor">            { \</span></div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;<span class="preprocessor">                return std::make_pair(false, defaultValue); \</span></div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;<span class="preprocessor">            } \</span></div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;<span class="preprocessor">        }, \</span></div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;<span class="preprocessor">        DEFAULT_VALUE)</span></div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;</div><div class="line"><a name="l00257"></a><span class="lineno"><a class="line" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">  257</a></span>&#160;<span class="preprocessor">#define GET_OPTIONAL_WITH_FALLBACK(PARAM, \</span></div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;<span class="preprocessor">                                   PARAM_TYPE, \</span></div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;<span class="preprocessor">                                   OPTIONAL_VALUE, \</span></div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;<span class="preprocessor">                                   FALLBACK_VALUE, \</span></div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;<span class="preprocessor">                                   VALUE_TYPE, \</span></div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;<span class="preprocessor">                                   DEFAULT_VALUE) \</span></div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;<span class="preprocessor">    GetOptionalWithFallback( \</span></div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;<span class="preprocessor">        PARAM, \</span></div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;<span class="preprocessor">        [](const PARAM_TYPE &amp; param, VALUE_TYPE defaultValue) \</span></div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;<span class="preprocessor">        { \</span></div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;<span class="preprocessor">            if (param.has_##OPTIONAL_VALUE ()) \</span></div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;<span class="preprocessor">            { \</span></div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;<span class="preprocessor">                return std::make_pair(true, param.OPTIONAL_VALUE ()); \</span></div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;<span class="preprocessor">            } \</span></div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;<span class="preprocessor">            else \</span></div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;<span class="preprocessor">            { \</span></div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;<span class="preprocessor">                return std::make_pair(false, defaultValue); \</span></div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;<span class="preprocessor">            } \</span></div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;<span class="preprocessor">        }, \</span></div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;<span class="preprocessor">        [](const PARAM_TYPE &amp; param, VALUE_TYPE defaultValue) \</span></div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;<span class="preprocessor">        { \</span></div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;<span class="preprocessor">            if (param.has_##FALLBACK_VALUE ()) \</span></div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;<span class="preprocessor">            { \</span></div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;<span class="preprocessor">                return std::make_pair(true, param.FALLBACK_VALUE ()); \</span></div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;<span class="preprocessor">            } \</span></div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;<span class="preprocessor">            else \</span></div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;<span class="preprocessor">            { \</span></div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;<span class="preprocessor">                return std::make_pair(false, defaultValue); \</span></div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;<span class="preprocessor">            } \</span></div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;<span class="preprocessor">        }, \</span></div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;<span class="preprocessor">        DEFAULT_VALUE)</span></div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;} <span class="comment">// namespace &lt;anonymous&gt;</span></div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;<span class="keyword">const</span> std::map&lt;std::string, ICaffeParser::CaffeParserImpl::OperationParsingFunction&gt;</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    ICaffeParser::CaffeParserImpl::ms_CaffeLayerNameToParsingFunctions = {</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;    { <span class="stringliteral">&quot;Input&quot;</span>,        &amp;CaffeParserImpl::ParseInputLayer },</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    { <span class="stringliteral">&quot;Convolution&quot;</span>,  &amp;CaffeParserImpl::ParseConvLayer },</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;    { <span class="stringliteral">&quot;Deconvolution&quot;</span>,&amp;CaffeParserImpl::ParseDeconvLayer },</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    { <span class="stringliteral">&quot;Pooling&quot;</span>,      &amp;CaffeParserImpl::ParsePoolingLayer },</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    { <span class="stringliteral">&quot;ReLU&quot;</span>,         &amp;CaffeParserImpl::ParseReluLayer },</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    { <span class="stringliteral">&quot;LRN&quot;</span>,          &amp;CaffeParserImpl::ParseLRNLayer },</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    { <span class="stringliteral">&quot;InnerProduct&quot;</span>, &amp;CaffeParserImpl::ParseInnerProductLayer },</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    { <span class="stringliteral">&quot;Softmax&quot;</span>,      &amp;CaffeParserImpl::ParseSoftmaxLayer },</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    { <span class="stringliteral">&quot;Eltwise&quot;</span>,      &amp;CaffeParserImpl::ParseEltwiseLayer },</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    { <span class="stringliteral">&quot;Concat&quot;</span>,       &amp;CaffeParserImpl::ParseConcatLayer },</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    { <span class="stringliteral">&quot;BatchNorm&quot;</span>,    &amp;CaffeParserImpl::ParseBatchNormLayer },</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    { <span class="stringliteral">&quot;Scale&quot;</span>,        &amp;CaffeParserImpl::ParseScaleLayer },</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;    { <span class="stringliteral">&quot;Split&quot;</span>,        &amp;CaffeParserImpl::ParseSplitLayer },</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;    { <span class="stringliteral">&quot;Dropout&quot;</span>,      &amp;CaffeParserImpl::ParseDropoutLayer},</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    { <span class="stringliteral">&quot;ArgMax&quot;</span>,       &amp;CaffeParserImpl::ParseArgmaxLayer},</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;};</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;</div><div class="line"><a name="l00310"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a4ddfa6cb51f928114a8151b8f455f115">  310</a></span>&#160;ICaffeParser::CaffeParserImpl::CaffeParserImpl()</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    : m_Network(nullptr, nullptr)</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;{</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;}</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;</div><div class="line"><a name="l00316"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_caffe_parser.xhtml#a82fc903eb5648250a6d82371a94772a3">  316</a></span>&#160;<a class="code" href="classarmnn_caffe_parser_1_1_caffe_parser.xhtml#a82fc903eb5648250a6d82371a94772a3">CaffeParser::CaffeParser</a>()</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;: <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a4ddfa6cb51f928114a8151b8f455f115">CaffeParserImpl</a>()</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;{</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;}</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;</div><div class="line"><a name="l00322"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a8b053a6c449d0814cc831c916c126668">  322</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">BindingPointInfo</a> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a8b053a6c449d0814cc831c916c126668">ICaffeParser::CaffeParserImpl::GetNetworkInputBindingInfo</a>(<span class="keyword">const</span> std::string&amp; name)<span class="keyword"> const</span></div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#afb7e4da478bab76261963479baad5788">GetBindingInfo</a>(name, <span class="stringliteral">&quot;input&quot;</span>, <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac62e2558c14e01605f2b4e1e21cdd1e8">m_NetworkInputsBindingInfo</a>);</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;}</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;</div><div class="line"><a name="l00327"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a4b1fdcb1985af12dd1848a9ffa5d3271">  327</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">BindingPointInfo</a> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a4b1fdcb1985af12dd1848a9ffa5d3271">ICaffeParser::CaffeParserImpl::GetNetworkOutputBindingInfo</a>(<span class="keyword">const</span> std::string&amp; name)<span class="keyword"> const</span></div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#afb7e4da478bab76261963479baad5788">GetBindingInfo</a>(name, <span class="stringliteral">&quot;output&quot;</span>, <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a62d6d6cba9ed0d3ad63fffb40aec86b5">m_NetworkOutputsBindingInfo</a>);</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;}</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;</div><div class="line"><a name="l00332"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#afb7e4da478bab76261963479baad5788">  332</a></span>&#160;std::pair&lt;armnn::LayerBindingId, armnn::TensorInfo&gt; <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#afb7e4da478bab76261963479baad5788">ICaffeParser::CaffeParserImpl::GetBindingInfo</a>(</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    <span class="keyword">const</span> std::string&amp; layerName,</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">char</span>* bindingPointDesc,</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    <span class="keyword">const</span> std::unordered_map&lt;std::string, BindingPointInfo&gt;&amp; nameToBindingInfo)</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;{</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    <span class="keyword">auto</span> it = nameToBindingInfo.find(layerName);</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    <span class="keywordflow">if</span> (it == nameToBindingInfo.end())</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    {</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;            fmt::format(<span class="stringliteral">&quot;Unknown binding {} for layer &#39;{}&#39;. {}&quot;</span>,</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;                        bindingPointDesc,</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;                        layerName,</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;    }</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;    <span class="keywordflow">return</span> it-&gt;second;</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;}</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;</div><div class="line"><a name="l00349"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">  349</a></span>&#160;<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">ICaffeParser::CaffeParserImpl::BlobShapeToTensorInfo</a>(<span class="keyword">const</span> caffe::BlobShape&amp; blobShape)<span class="keyword"> const</span></div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;    std::vector&lt;unsigned int&gt; shape;</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; blobShape.dim_size(); ++j)</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;    {</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;        shape.push_back(static_cast&lt;unsigned int&gt;(blobShape.dim(j)));</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;    }</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(armnn::numeric_cast&lt;unsigned int&gt;(shape.size()), shape.data(), DataType::Float32);</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;}</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;</div><div class="line"><a name="l00360"></a><span class="lineno"><a class="line" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">  360</a></span>&#160;BlobShape <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; desc)</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;{</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;    BlobShape ret;</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; desc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;    {</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;        ret.add_dim(i);</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;        ret.set_dim(armnn::numeric_cast&lt;int&gt;(i), desc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]);</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;    }</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;    <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;}</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;<span class="comment">// Note: can move to CaffeParser when/if we optimise the text/string format</span></div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;<span class="comment">//       to load on a layer by layer basis</span></div><div class="line"><a name="l00374"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2f8fbd66c1a39a06d61fcb6536387d64">  374</a></span>&#160;vector&lt;const LayerParameter*&gt; <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2f8fbd66c1a39a06d61fcb6536387d64">ICaffeParser::CaffeParserImpl::GetInputs</a>(<span class="keyword">const</span> LayerParameter&amp; layerParam)</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;{</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;    std::vector&lt;const caffe::LayerParameter*&gt; ret;</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;    ret.reserve(armnn::numeric_cast&lt;size_t&gt;(layerParam.bottom_size()));</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; layerParam.bottom_size(); ++j)</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;    {</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;        std::string inputName = layerParam.bottom(j);</div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;        <span class="keyword">auto</span> inputIt = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1424f1bfbfc81d317b51053bbb315ef1">m_CaffeLayersByTopName</a>.find(inputName);</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;        <span class="keywordflow">if</span> (inputIt == <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1424f1bfbfc81d317b51053bbb315ef1">m_CaffeLayersByTopName</a>.end())</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;        {</div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;                fmt::format(<span class="stringliteral">&quot;Can&#39;t find Caffe layer with top called &#39;{}&#39;, &quot;</span></div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;                            <span class="stringliteral">&quot;which is listed as an input of &#39;{}&#39;. {}&quot;</span>,</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;                            inputName,</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;                            layerParam.name(),</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;                            <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;        }</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;        ret.push_back(inputIt-&gt;second);</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    }</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;    <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;}</div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;</div><div class="line"><a name="l00397"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a3a2636dd8414f2bb62c5fa097bdc9791">  397</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a3a2636dd8414f2bb62c5fa097bdc9791">ICaffeParser::CaffeParserImpl::ParseInputLayer</a>(<span class="keyword">const</span> LayerParameter&amp; layerParam)</div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;{</div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;    <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layerParam.type() == <span class="stringliteral">&quot;Input&quot;</span>);</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;    ValidateNumInputsOutputs(layerParam, 0, 1);</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;</div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;    <span class="keyword">const</span> InputParameter&amp; param = layerParam.input_param();</div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;</div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> inputId = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a>&lt;<a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a>&gt;(</div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;        <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac62e2558c14e01605f2b4e1e21cdd1e8">m_NetworkInputsBindingInfo</a>.size());</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddInputLayer(inputId, layerParam.name().c_str());</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;    <span class="comment">// Decides the tensor info for this input. This can be specified in the Caffe network but can also</span></div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;    <span class="comment">// be overriden by user input (m_inputShapes).</span></div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;    <span class="keyword">const</span> BlobShape* originalShape = param.shape_size() &gt; 0 &amp;&amp; param.shape(0).dim_size() &gt; 0 ?</div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;        &amp;param.shape(0) : <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;    <span class="keywordflow">if</span> (originalShape)</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;    {</div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;        inputTensorInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(*originalShape);</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;    }</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;    <span class="keyword">auto</span> overrideIt = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a76ea67f3f7d1d5835c5a92b65dc0854c">m_InputShapes</a>.find(layerParam.name());</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;    <span class="keywordflow">if</span> (overrideIt != <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a76ea67f3f7d1d5835c5a92b65dc0854c">m_InputShapes</a>.end())</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;    {</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>&amp; overrideShape = overrideIt-&gt;second;</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;        <span class="keywordflow">if</span> (originalShape &amp;&amp;</div><div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;            (    originalShape-&gt;dim(1) != overrideShape[1]</div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;              || originalShape-&gt;dim(2) != overrideShape[2]</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;              || originalShape-&gt;dim(3) != overrideShape[3]))</div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;        {</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;                fmt::format(<span class="stringliteral">&quot;Parsed input shape for &#39;{}&#39; is incompatible with the override provided. {}&quot;</span>,</div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;                            layerParam.name(),</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;                            <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;        }</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;        inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(overrideShape);</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;    }</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span> (!originalShape)</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;    {</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;            fmt::format(<span class="stringliteral">&quot;No input descriptor given for &#39;{}&#39; and no input shape found in caffe model. {}&quot;</span>,</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;    }</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2a1112c66d08e3760ecccf39c7854a90">TrackInputBinding</a>(inputLayer, inputId, inputTensorInfo);</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;    inputLayer-&gt;GetOutputSlot(0).SetTensorInfo(inputTensorInfo);</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), inputLayer-&gt;GetOutputSlot(0));</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;}</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;</div><div class="line"><a name="l00447"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9fea304829fe514d664de515ca5c3918">  447</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9fea304829fe514d664de515ca5c3918">ICaffeParser::CaffeParserImpl::AddConvLayerWithSplits</a>(<span class="keyword">const</span> caffe::LayerParameter&amp; layerParam,</div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;                                                           <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a>&amp; desc,</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;                                                           <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelW,</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;                                                           <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelH)</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;{</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;    <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layerParam.type() == <span class="stringliteral">&quot;Convolution&quot;</span>);</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;    ConvolutionParameter convParam = layerParam.convolution_param();</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;    BlobShape inputShape = <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>());</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numGroups = convParam.has_group() ? convParam.group() : 1;</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;    <span class="comment">// asusme these were already verified by the caller ParseConvLayer() function</span></div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;    <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numGroups &lt; inputShape.dim(1));</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;    <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numGroups &gt; 1);</div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;    <span class="comment">// Handle grouping</span></div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;    <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>&amp; inputConnection = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0));</div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160; 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                                       static_cast&lt;unsigned int&gt;(inputShape.dim(1)),</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;                                        static_cast&lt;unsigned int&gt;(inputShape.dim(2)),</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;                                        static_cast&lt;unsigned int&gt;(inputShape.dim(3))};</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;    <span class="comment">// Split dimension 1 of the splitter output shape and conv input shapes</span></div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;    <span class="comment">// according to the number of groups</span></div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;    splitterDimSizes[1] /= numGroups;</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;    inputShape.set_dim(1, splitterDimSizes[1]);</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;    <span class="comment">// This is used to describe how the input is to be split</span></div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;    <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">ViewsDescriptor</a> splitterDesc(numGroups);</div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;</div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;    <span class="comment">// Create an output node for each group, giving each a unique name</span></div><div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g &lt; numGroups; ++g)</div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;    {</div><div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;        <span class="comment">// Work out the names of the splitter layers child convolutions</span></div><div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;        stringstream ss;</div><div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;        ss &lt;&lt; layerParam.name() &lt;&lt; <span class="stringliteral">&quot;_&quot;</span> &lt;&lt; g;</div><div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;        convLayerNames[g] = ss.str();</div><div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;</div><div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;        splitterDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(g, 1, splitterDimSizes[1] * g);</div><div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;</div><div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;        <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx=0; dimIdx &lt; 4; dimIdx++)</div><div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;        {</div><div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;            splitterDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(g, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;        }</div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;    }</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;</div><div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;    <span class="keyword">const</span> std::string splitterLayerName = std::string(<span class="stringliteral">&quot;splitter_&quot;</span>) + layerParam.bottom(0);</div><div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* splitterLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddSplitterLayer(splitterDesc, splitterLayerName.c_str());</div><div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;</div><div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;    inputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); i++)</div><div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;    {</div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;        splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(inputShape));</div><div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;    }</div><div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;</div><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numFilters = convParam.num_output();</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;</div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;    <span class="comment">// Populates convolution output tensor descriptor dimensions.</span></div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;    BlobShape outputShape;</div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;    outputShape.add_dim(0);</div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;    outputShape.set_dim(0, inputShape.dim(0));</div><div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;    outputShape.add_dim(1);</div><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;    <span class="comment">// Ensures that dimension 1 of the convolution output is split according to the number of groups.</span></div><div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;    outputShape.set_dim(1, numFilters / numGroups);</div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;    outputShape.add_dim(2);</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;    outputShape.set_dim(</div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;        2, (static_cast&lt;int&gt;(</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;                static_cast&lt;float&gt;(inputShape.dim(2) + 2 * desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> - (desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> * (kernelH - 1) + 1)) /</div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;                static_cast&lt;float&gt;(desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>)) + 1));</div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;    outputShape.add_dim(3);</div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;    outputShape.set_dim(</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;        3, (static_cast&lt;int&gt;(</div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;                static_cast&lt;float&gt;(inputShape.dim(3) + 2 * desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> - (desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> * (kernelW - 1)  + 1)) /</div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;                static_cast&lt;float&gt;(desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>)) + 1));</div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;</div><div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160; 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   GetDataFromBlob(layerParam, weightData, 0);</div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;</div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightDimSizes[4] = {</div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;        <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(outputShape.dim(1)),</div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;        static_cast&lt;unsigned int&gt;(inputShape.dim(1)),</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;        kernelH,</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;        kernelW};</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160; 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       <span class="comment">// group 1 should be connected to the 1st output of the splitter.</span></div><div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;</div><div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;        <span class="comment">// Pulls out the weights for this group from that loaded from the model file earlier.</span></div><div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(4, weightDimSizes, DataType::Float32),</div><div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;                            weightData.data() + numWeightsPerGroup * g);</div><div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;</div><div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* convLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;        <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a> optionalBiases;</div><div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;        <span class="keywordflow">if</span> (desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;        {</div><div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;            <span class="comment">// Pulls out the biases for this group from that loaded from the model file earlier.</span></div><div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;            <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(biasInfo, biasData.data() + numBiasesPerGroup * g);</div><div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;            optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a>(biases);</div><div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;        }</div><div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;        convLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddConvolution2dLayer(desc,</div><div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;                                                     weights,</div><div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;                                                     optionalBiases,</div><div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;                                                     convLayerNames[g].c_str());</div><div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;        convLayers[g] = convLayer;</div><div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;</div><div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;        <span class="comment">// If we have more than one group then the input to the nth convolution the splitter layer&#39;s nth output,</span></div><div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;        <span class="comment">// otherwise it&#39;s the regular input to this layer.</span></div><div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;        <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>&amp; splitterInputConnection =</div><div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;            splitterLayer ? splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(g) : inputConnection;</div><div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;        splitterInputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;        convLayer-&gt;GetOutputSlot(0).SetTensorInfo(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(outputShape));</div><div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;    }</div><div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;</div><div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;    <span class="comment">// If the convolution was performed in chunks, add a layer to concatenate the results</span></div><div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;</div><div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;    <span class="comment">// The merge input shape matches that of the convolution output</span></div><div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDimSizes[4] = {<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(outputShape.dim(0)),</div><div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;                                      static_cast&lt;unsigned int&gt;(outputShape.dim(1)),</div><div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;                                      static_cast&lt;unsigned int&gt;(outputShape.dim(2)),</div><div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;                                      static_cast&lt;unsigned int&gt;(outputShape.dim(3))};</div><div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;</div><div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;    <span class="comment">// This is used to describe how the input is to be concatenated</span></div><div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;    <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> concatDesc(numGroups);</div><div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;</div><div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;    <span class="comment">// Now create an input node for each group, using the name from</span></div><div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;    <span class="comment">// the output of the corresponding convolution</span></div><div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g &lt; numGroups; ++g)</div><div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;    {</div><div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;        concatDesc.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(g, 1, concatDimSizes[1] * g);</div><div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;    }</div><div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;</div><div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;    <span class="comment">// Make sure the output from the concat is the correct size to hold the data for all groups</span></div><div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;    concatDimSizes[1] *= numGroups;</div><div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;    outputShape.set_dim(1, concatDimSizes[1]);</div><div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;</div><div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;    <span class="comment">// Finally add the concat layer</span></div><div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* concatLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddConcatLayer(concatDesc, layerParam.name().c_str());</div><div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;</div><div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;    <span class="keywordflow">if</span> (!concatLayer)</div><div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;    {</div><div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;            fmt::format(<span class="stringliteral">&quot;Failed to create final concat layer for Split+Convolution+Concat. &quot;</span></div><div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;                        <span class="stringliteral">&quot;Layer={} #groups={} #filters={} {}&quot;</span>,</div><div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;                        numGroups,</div><div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;                        numFilters,</div><div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;    }</div><div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;</div><div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g &lt; numGroups; ++g)</div><div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;    {</div><div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;        convLayers[g]-&gt;GetOutputSlot(0).Connect(concatLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(g));</div><div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;    }</div><div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;    concatLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, concatDimSizes, DataType::Float32));</div><div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), concatLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;}</div><div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;</div><div class="line"><a name="l00637"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77f532a6a82afeb6e79957726a9517a5">  637</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77f532a6a82afeb6e79957726a9517a5">ICaffeParser::CaffeParserImpl::AddDeconvLayerWithSplits</a>(<span class="keyword">const</span> caffe::LayerParameter&amp; layerParam,</div><div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;                                                             <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a>&amp; desc,</div><div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;                                                             <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelW,</div><div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;                                                             <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelH)</div><div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;{</div><div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;    <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layerParam.type() == <span class="stringliteral">&quot;Deconvolution&quot;</span>);</div><div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;</div><div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;    ConvolutionParameter convParam = layerParam.convolution_param();</div><div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;    BlobShape inputShape = <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>());</div><div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numGroups = convParam.has_group() ? convParam.group() : 1;</div><div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;</div><div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;    <span class="comment">// asusme these were already verified by the caller ParseDeconvLayer() function</span></div><div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;    <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numGroups &lt;= inputShape.dim(1));</div><div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;    <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numGroups &gt; 1);</div><div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;</div><div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;    <span class="comment">// Handle grouping</span></div><div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;    <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>&amp; inputConnection = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0));</div><div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;</div><div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;    vector&lt;string&gt; convLayerNames(numGroups);</div><div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;    vector&lt;armnn::IConnectableLayer*&gt; convLayers(numGroups);</div><div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;    convLayerNames[0] = layerParam.name();</div><div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;</div><div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;    <span class="comment">// This deconvolution is to be applied to chunks of the input data so add a splitter layer</span></div><div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;</div><div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;    <span class="comment">// Redirect the deconvolution input to the splitter</span></div><div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitterDimSizes[4] = {<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(inputShape.dim(0)),</div><div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;                                        static_cast&lt;unsigned int&gt;(inputShape.dim(1)),</div><div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;                                        static_cast&lt;unsigned int&gt;(inputShape.dim(2)),</div><div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;                                        static_cast&lt;unsigned int&gt;(inputShape.dim(3))};</div><div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;</div><div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;    <span class="comment">// Split dimension 1 of the splitter output shape and deconv input shapes</span></div><div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;    <span class="comment">// according to the number of groups</span></div><div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;</div><div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160; 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g &lt; numGroups; ++g)</div><div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;    {</div><div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;        <span class="comment">// Work out the names of the splitter layers child deconvolutions</span></div><div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160;        stringstream ss;</div><div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;        ss &lt;&lt; layerParam.name() &lt;&lt; <span class="stringliteral">&quot;_&quot;</span> &lt;&lt; g;</div><div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;        convLayerNames[g] = ss.str();</div><div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;</div><div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160;        splitterDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(g, 1, splitterDimSizes[1] * g);</div><div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;</div><div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;        <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx=0; dimIdx &lt; 4; dimIdx++)</div><div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;        {</div><div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160;            splitterDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(g, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;        }</div><div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;    }</div><div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;</div><div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;    <span class="keyword">const</span> std::string splitterLayerName = std::string(<span class="stringliteral">&quot;splitter_&quot;</span>) + layerParam.bottom(0);</div><div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* splitterLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddSplitterLayer(splitterDesc, splitterLayerName.c_str());</div><div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;</div><div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;    inputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); i++)</div><div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160;    {</div><div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;        splitterLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(inputShape));</div><div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;    }</div><div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;</div><div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numFilters = convParam.num_output();</div><div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;</div><div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;    <span class="comment">// Populates deconvolution output tensor descriptor dimensions.</span></div><div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160;    BlobShape outputShape;</div><div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160;    outputShape.add_dim(0);</div><div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;    outputShape.set_dim(0, inputShape.dim(0));</div><div class="line"><a name="l00709"></a><span class="lineno">  709</span>&#160;    outputShape.add_dim(1);</div><div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160;    <span class="comment">// Ensures that dimension 1 of the deconvolution output is split according to the number of groups.</span></div><div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;    outputShape.set_dim(1, numFilters / numGroups);</div><div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160;    outputShape.add_dim(2);</div><div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;    outputShape.set_dim(</div><div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160;        2, (static_cast&lt;int&gt;(</div><div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;                desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> * (inputShape.dim(2) - 1) - 2 * desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> + kernelH)));</div><div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160;    outputShape.add_dim(3);</div><div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;    outputShape.set_dim(</div><div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160;        3, (static_cast&lt;int&gt;(</div><div class="line"><a name="l00719"></a><span class="lineno">  719</span>&#160;                desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> * (inputShape.dim(3) - 1) - 2 * desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> + kernelW)));</div><div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;</div><div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160;    <span class="comment">// Load the weight data for ALL groups</span></div><div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;    vector&lt;float&gt; weightData(armnn::numeric_cast&lt;size_t&gt;(numGroups *</div><div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160;                                                         inputShape.dim(1) *  <span class="comment">// number of input channels</span></div><div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;                                                         outputShape.dim(1) * <span class="comment">// number of output channels</span></div><div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;                                                         kernelH *</div><div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;                                                         kernelW));</div><div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;    GetDataFromBlob(layerParam, weightData, 0);</div><div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160;</div><div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightDimSizes[4] = {</div><div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;        <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(outputShape.dim(1)),</div><div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160;        static_cast&lt;unsigned int&gt;(inputShape.dim(1)),</div><div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;        kernelH,</div><div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;        kernelW};</div><div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;</div><div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo;</div><div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;    vector&lt;float&gt; biasData;</div><div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160;</div><div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;    <span class="keywordflow">if</span> (desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;    {</div><div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;        biasData.resize(armnn::numeric_cast&lt;size_t&gt;(numGroups * outputShape.dim(1)), 1.f);</div><div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;        GetDataFromBlob(layerParam, biasData, 1);</div><div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160;</div><div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasDimSizes[1] = {<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(outputShape.dim(1))};</div><div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;        biasInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, biasDimSizes, DataType::Float32);</div><div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;    }</div><div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;</div><div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numWeightsPerGroup = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(weightData.size()) / numGroups;</div><div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numBiasesPerGroup  = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(biasData.size()) / numGroups;</div><div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160;</div><div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g &lt; numGroups; ++g)</div><div class="line"><a name="l00751"></a><span class="lineno">  751</span>&#160;    {</div><div class="line"><a name="l00752"></a><span class="lineno">  752</span>&#160;        <span class="comment">// Sets the slot index, group 0 should be connected to the 0th output of the splitter</span></div><div class="line"><a name="l00753"></a><span class="lineno">  753</span>&#160;        <span class="comment">// group 1 should be connected to the 1st output of the splitter.</span></div><div class="line"><a name="l00754"></a><span class="lineno">  754</span>&#160;</div><div class="line"><a name="l00755"></a><span class="lineno">  755</span>&#160;        <span class="comment">// Pulls out the weights for this group from that loaded from the model file earlier.</span></div><div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160; 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       {</div><div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;            <span class="comment">// Pulls out the biases for this group from that loaded from the model file earlier.</span></div><div class="line"><a name="l00764"></a><span class="lineno">  764</span>&#160;            <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(biasInfo, biasData.data() + numBiasesPerGroup * g);</div><div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160;            optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a>(biases);</div><div class="line"><a name="l00766"></a><span class="lineno">  766</span>&#160;        }</div><div class="line"><a name="l00767"></a><span class="lineno">  767</span>&#160;        deconvLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddTransposeConvolution2dLayer(desc,</div><div class="line"><a name="l00768"></a><span class="lineno">  768</span>&#160;                                                     weights,</div><div class="line"><a name="l00769"></a><span class="lineno">  769</span>&#160;                                                     optionalBiases,</div><div class="line"><a name="l00770"></a><span class="lineno">  770</span>&#160;                                                     convLayerNames[g].c_str());</div><div class="line"><a name="l00771"></a><span class="lineno">  771</span>&#160;        convLayers[g] = deconvLayer;</div><div class="line"><a name="l00772"></a><span class="lineno">  772</span>&#160;</div><div class="line"><a name="l00773"></a><span class="lineno">  773</span>&#160;        <span class="comment">// If we have more than one group then the input to the nth deconvolution the splitter layer&#39;s nth output,</span></div><div class="line"><a name="l00774"></a><span class="lineno">  774</span>&#160;        <span class="comment">// otherwise it&#39;s the regular input to this layer.</span></div><div class="line"><a name="l00775"></a><span class="lineno">  775</span>&#160; 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   }</div><div class="line"><a name="l00780"></a><span class="lineno">  780</span>&#160;</div><div class="line"><a name="l00781"></a><span class="lineno">  781</span>&#160;    <span class="comment">// If the deconvolution was performed in chunks, add a layer to concatenate the results</span></div><div class="line"><a name="l00782"></a><span class="lineno">  782</span>&#160;</div><div class="line"><a name="l00783"></a><span class="lineno">  783</span>&#160;    <span class="comment">// The merge input shape matches that of the deconvolution output</span></div><div class="line"><a name="l00784"></a><span class="lineno">  784</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDimSizes[4] = {<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(outputShape.dim(0)),</div><div class="line"><a name="l00785"></a><span class="lineno">  785</span>&#160;                                      static_cast&lt;unsigned int&gt;(outputShape.dim(1)),</div><div class="line"><a name="l00786"></a><span class="lineno">  786</span>&#160;                                      static_cast&lt;unsigned int&gt;(outputShape.dim(2)),</div><div class="line"><a name="l00787"></a><span class="lineno">  787</span>&#160;                                      static_cast&lt;unsigned int&gt;(outputShape.dim(3))};</div><div class="line"><a name="l00788"></a><span class="lineno">  788</span>&#160;</div><div class="line"><a name="l00789"></a><span class="lineno">  789</span>&#160;    <span class="comment">// This is used to describe how the input is to be concatenated</span></div><div class="line"><a name="l00790"></a><span class="lineno">  790</span>&#160;    <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> concatDesc(numGroups);</div><div class="line"><a name="l00791"></a><span class="lineno">  791</span>&#160;</div><div class="line"><a name="l00792"></a><span class="lineno">  792</span>&#160; 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                       numFilters,</div><div class="line"><a name="l00814"></a><span class="lineno">  814</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00815"></a><span class="lineno">  815</span>&#160;    }</div><div class="line"><a name="l00816"></a><span class="lineno">  816</span>&#160;</div><div class="line"><a name="l00817"></a><span class="lineno">  817</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g &lt; numGroups; ++g)</div><div class="line"><a name="l00818"></a><span class="lineno">  818</span>&#160;    {</div><div class="line"><a name="l00819"></a><span class="lineno">  819</span>&#160;        convLayers[g]-&gt;GetOutputSlot(0).Connect(concatLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(g));</div><div class="line"><a name="l00820"></a><span class="lineno">  820</span>&#160;    }</div><div class="line"><a name="l00821"></a><span class="lineno">  821</span>&#160;    concatLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, concatDimSizes, DataType::Float32));</div><div class="line"><a name="l00822"></a><span class="lineno">  822</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), concatLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l00823"></a><span class="lineno">  823</span>&#160;}</div><div class="line"><a name="l00824"></a><span class="lineno">  824</span>&#160;</div><div class="line"><a name="l00825"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#adcb87456482d5df17ef09eca1a808091">  825</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#adcb87456482d5df17ef09eca1a808091">ICaffeParser::CaffeParserImpl::AddConvLayerWithDepthwiseConv</a>(<span class="keyword">const</span> caffe::LayerParameter&amp; layerParam,</div><div class="line"><a name="l00826"></a><span class="lineno">  826</span>&#160;                                                                  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a>&amp; convDesc,</div><div class="line"><a name="l00827"></a><span class="lineno">  827</span>&#160;                                                                  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelW,</div><div class="line"><a name="l00828"></a><span class="lineno">  828</span>&#160;                                                                  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelH)</div><div class="line"><a name="l00829"></a><span class="lineno">  829</span>&#160;{</div><div class="line"><a name="l00830"></a><span class="lineno">  830</span>&#160;    <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layerParam.type() == <span class="stringliteral">&quot;Convolution&quot;</span>);</div><div class="line"><a name="l00831"></a><span class="lineno">  831</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l00832"></a><span class="lineno">  832</span>&#160;</div><div class="line"><a name="l00833"></a><span class="lineno">  833</span>&#160;    ConvolutionParameter convParam  = layerParam.convolution_param();</div><div class="line"><a name="l00834"></a><span class="lineno">  834</span>&#160;    BlobShape inputShape = <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>());</div><div class="line"><a name="l00835"></a><span class="lineno">  835</span>&#160;</div><div class="line"><a name="l00836"></a><span class="lineno">  836</span>&#160;    <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> desc;</div><div class="line"><a name="l00837"></a><span class="lineno">  837</span>&#160;    desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>      = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>;</div><div class="line"><a name="l00838"></a><span class="lineno">  838</span>&#160;    desc.m_PadRight     = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>;</div><div class="line"><a name="l00839"></a><span class="lineno">  839</span>&#160;    desc.m_PadTop       = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>;</div><div class="line"><a name="l00840"></a><span class="lineno">  840</span>&#160;    desc.m_PadBottom    = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>;</div><div class="line"><a name="l00841"></a><span class="lineno">  841</span>&#160;    desc.m_StrideX      = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>;</div><div class="line"><a name="l00842"></a><span class="lineno">  842</span>&#160;    desc.m_StrideY      = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>;</div><div class="line"><a name="l00843"></a><span class="lineno">  843</span>&#160;    desc.m_DilationX    = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a>;</div><div class="line"><a name="l00844"></a><span class="lineno">  844</span>&#160;    desc.m_DilationY    = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a>;</div><div class="line"><a name="l00845"></a><span class="lineno">  845</span>&#160;    desc.m_BiasEnabled  = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>;</div><div class="line"><a name="l00846"></a><span class="lineno">  846</span>&#160;</div><div class="line"><a name="l00847"></a><span class="lineno">  847</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numFilters = convParam.num_output();</div><div class="line"><a name="l00848"></a><span class="lineno">  848</span>&#160;</div><div class="line"><a name="l00849"></a><span class="lineno">  849</span>&#160;    BlobShape outputShape;</div><div class="line"><a name="l00850"></a><span class="lineno">  850</span>&#160;    outputShape.add_dim(0);</div><div class="line"><a name="l00851"></a><span class="lineno">  851</span>&#160;    outputShape.set_dim(0, inputShape.dim(0));</div><div class="line"><a name="l00852"></a><span class="lineno">  852</span>&#160;    outputShape.add_dim(1);</div><div class="line"><a name="l00853"></a><span class="lineno">  853</span>&#160;    outputShape.set_dim(1, numFilters);</div><div class="line"><a name="l00854"></a><span class="lineno">  854</span>&#160;    outputShape.add_dim(2);</div><div class="line"><a name="l00855"></a><span class="lineno">  855</span>&#160;    outputShape.set_dim(</div><div class="line"><a name="l00856"></a><span class="lineno">  856</span>&#160;        2, (static_cast&lt;int&gt;(</div><div class="line"><a name="l00857"></a><span class="lineno">  857</span>&#160;                static_cast&lt;float&gt;(inputShape.dim(2) + 2 * desc.m_PadBottom - (desc.m_DilationX * (kernelH - 1) + 1)) /</div><div class="line"><a name="l00858"></a><span class="lineno">  858</span>&#160;                static_cast&lt;float&gt;(desc.m_StrideY)) + 1));</div><div class="line"><a name="l00859"></a><span class="lineno">  859</span>&#160;    outputShape.add_dim(3);</div><div class="line"><a name="l00860"></a><span class="lineno">  860</span>&#160;    outputShape.set_dim(</div><div class="line"><a name="l00861"></a><span class="lineno">  861</span>&#160;        3, (static_cast&lt;int&gt;(</div><div class="line"><a name="l00862"></a><span class="lineno">  862</span>&#160;                static_cast&lt;float&gt;(inputShape.dim(3) + 2 * desc.m_PadRight - (desc.m_DilationY * (kernelW - 1) + 1)) /</div><div class="line"><a name="l00863"></a><span class="lineno">  863</span>&#160;                static_cast&lt;float&gt;(desc.m_StrideX)) + 1));</div><div class="line"><a name="l00864"></a><span class="lineno">  864</span>&#160;</div><div class="line"><a name="l00865"></a><span class="lineno">  865</span>&#160;    <span class="comment">// Load the weight data</span></div><div class="line"><a name="l00866"></a><span class="lineno">  866</span>&#160;    <span class="keywordtype">size_t</span> allWeightsSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a>&lt;<span class="keywordtype">size_t</span>&gt;(inputShape.dim(1) * kernelH * kernelW);</div><div class="line"><a name="l00867"></a><span class="lineno">  867</span>&#160;    vector&lt;float&gt; weightData(allWeightsSize);</div><div class="line"><a name="l00868"></a><span class="lineno">  868</span>&#160;</div><div class="line"><a name="l00869"></a><span class="lineno">  869</span>&#160;    GetDataFromBlob(layerParam, weightData, 0);</div><div class="line"><a name="l00870"></a><span class="lineno">  870</span>&#160;</div><div class="line"><a name="l00871"></a><span class="lineno">  871</span>&#160;    <span class="comment">// depth multiplier will be 1 for the depthwise convolution</span></div><div class="line"><a name="l00872"></a><span class="lineno">  872</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightDimSizes[4] = {</div><div class="line"><a name="l00873"></a><span class="lineno">  873</span>&#160;        <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(1),                 <span class="comment">// depth multiplier</span></div><div class="line"><a name="l00874"></a><span class="lineno">  874</span>&#160;        static_cast&lt;unsigned int&gt;(inputShape.dim(1)), <span class="comment">// #channels</span></div><div class="line"><a name="l00875"></a><span class="lineno">  875</span>&#160;        kernelH,</div><div class="line"><a name="l00876"></a><span class="lineno">  876</span>&#160;        kernelW};</div><div class="line"><a name="l00877"></a><span class="lineno">  877</span>&#160;</div><div class="line"><a name="l00878"></a><span class="lineno">  878</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* returnLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00879"></a><span class="lineno">  879</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(4, weightDimSizes, DataType::Float32), weightData.data());</div><div class="line"><a name="l00880"></a><span class="lineno">  880</span>&#160;    <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a> optionalBiases;</div><div class="line"><a name="l00881"></a><span class="lineno">  881</span>&#160;    vector&lt;float&gt; biasData;</div><div class="line"><a name="l00882"></a><span class="lineno">  882</span>&#160;    <span class="keywordflow">if</span> (desc.m_BiasEnabled)</div><div class="line"><a name="l00883"></a><span class="lineno">  883</span>&#160;    {</div><div class="line"><a name="l00884"></a><span class="lineno">  884</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo;</div><div class="line"><a name="l00885"></a><span class="lineno">  885</span>&#160;</div><div class="line"><a name="l00886"></a><span class="lineno">  886</span>&#160;        biasData.resize(armnn::numeric_cast&lt;size_t&gt;(outputShape.dim(1)), 1.f);</div><div class="line"><a name="l00887"></a><span class="lineno">  887</span>&#160;        GetDataFromBlob(layerParam, biasData, 1);</div><div class="line"><a name="l00888"></a><span class="lineno">  888</span>&#160;</div><div class="line"><a name="l00889"></a><span class="lineno">  889</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasDimSizes[1] = {<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(outputShape.dim(1))};</div><div class="line"><a name="l00890"></a><span class="lineno">  890</span>&#160;        biasInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, biasDimSizes, DataType::Float32);</div><div class="line"><a name="l00891"></a><span class="lineno">  891</span>&#160;</div><div class="line"><a name="l00892"></a><span class="lineno">  892</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(biasInfo, biasData.data());</div><div class="line"><a name="l00893"></a><span class="lineno">  893</span>&#160;        optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a>(biases);</div><div class="line"><a name="l00894"></a><span class="lineno">  894</span>&#160;    }</div><div class="line"><a name="l00895"></a><span class="lineno">  895</span>&#160;    returnLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddDepthwiseConvolution2dLayer(desc,</div><div class="line"><a name="l00896"></a><span class="lineno">  896</span>&#160;                                                            weights,</div><div class="line"><a name="l00897"></a><span class="lineno">  897</span>&#160;                                                            optionalBiases,</div><div class="line"><a name="l00898"></a><span class="lineno">  898</span>&#160;                                                            layerParam.name().c_str());</div><div class="line"><a name="l00899"></a><span class="lineno">  899</span>&#160;</div><div class="line"><a name="l00900"></a><span class="lineno">  900</span>&#160;    <span class="keywordflow">if</span> (!returnLayer)</div><div class="line"><a name="l00901"></a><span class="lineno">  901</span>&#160;    {</div><div class="line"><a name="l00902"></a><span class="lineno">  902</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00903"></a><span class="lineno">  903</span>&#160;            fmt::format(<span class="stringliteral">&quot;Failed to create depthwise convolution layer. &quot;</span></div><div class="line"><a name="l00904"></a><span class="lineno">  904</span>&#160;                        <span class="stringliteral">&quot;Layer={} #filters={} {}&quot;</span>,</div><div class="line"><a name="l00905"></a><span class="lineno">  905</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l00906"></a><span class="lineno">  906</span>&#160;                        numFilters,</div><div class="line"><a name="l00907"></a><span class="lineno">  907</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00908"></a><span class="lineno">  908</span>&#160;    }</div><div class="line"><a name="l00909"></a><span class="lineno">  909</span>&#160;    <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>&amp; inputConnection = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0));</div><div class="line"><a name="l00910"></a><span class="lineno">  910</span>&#160;    inputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(returnLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00911"></a><span class="lineno">  911</span>&#160;    returnLayer-&gt;GetOutputSlot(0).SetTensorInfo(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(outputShape));</div><div class="line"><a name="l00912"></a><span class="lineno">  912</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), returnLayer-&gt;GetOutputSlot(0));</div><div class="line"><a name="l00913"></a><span class="lineno">  913</span>&#160;}</div><div class="line"><a name="l00914"></a><span class="lineno">  914</span>&#160;</div><div class="line"><a name="l00915"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5cddc80538d5de7d36192e0fd2d09c63">  915</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5cddc80538d5de7d36192e0fd2d09c63">ICaffeParser::CaffeParserImpl::ParseConvLayer</a>(<span class="keyword">const</span> LayerParameter&amp; layerParam)</div><div class="line"><a name="l00916"></a><span class="lineno">  916</span>&#160;{</div><div class="line"><a name="l00917"></a><span class="lineno">  917</span>&#160;    <span class="comment">// Ignored Caffe Parameters</span></div><div class="line"><a name="l00918"></a><span class="lineno">  918</span>&#160;    <span class="comment">// * Weight Filler</span></div><div class="line"><a name="l00919"></a><span class="lineno">  919</span>&#160;    <span class="comment">// * Bias Filler</span></div><div class="line"><a name="l00920"></a><span class="lineno">  920</span>&#160;    <span class="comment">// * Engine</span></div><div class="line"><a name="l00921"></a><span class="lineno">  921</span>&#160;    <span class="comment">// * Force nd_im2col</span></div><div class="line"><a name="l00922"></a><span class="lineno">  922</span>&#160;    <span class="comment">// * Axis</span></div><div class="line"><a name="l00923"></a><span class="lineno">  923</span>&#160;</div><div class="line"><a name="l00924"></a><span class="lineno">  924</span>&#160;    <span class="comment">// Not Available ArmNN Interface Parameters</span></div><div class="line"><a name="l00925"></a><span class="lineno">  925</span>&#160;    <span class="comment">// * Rounding policy;</span></div><div class="line"><a name="l00926"></a><span class="lineno">  926</span>&#160;</div><div class="line"><a name="l00927"></a><span class="lineno">  927</span>&#160;    <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layerParam.type() == <span class="stringliteral">&quot;Convolution&quot;</span>);</div><div class="line"><a name="l00928"></a><span class="lineno">  928</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l00929"></a><span class="lineno">  929</span>&#160;</div><div class="line"><a name="l00930"></a><span class="lineno">  930</span>&#160;    ConvolutionParameter convParam = layerParam.convolution_param();</div><div class="line"><a name="l00931"></a><span class="lineno">  931</span>&#160;    BlobShape inputShape = <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>());</div><div class="line"><a name="l00932"></a><span class="lineno">  932</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numGroups = convParam.has_group() ? convParam.group() : 1;</div><div class="line"><a name="l00933"></a><span class="lineno">  933</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numFilters = convParam.num_output();</div><div class="line"><a name="l00934"></a><span class="lineno">  934</span>&#160;</div><div class="line"><a name="l00935"></a><span class="lineno">  935</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> notFound = std::numeric_limits&lt;unsigned int&gt;::max();</div><div class="line"><a name="l00936"></a><span class="lineno">  936</span>&#160;</div><div class="line"><a name="l00937"></a><span class="lineno">  937</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelH = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l00938"></a><span class="lineno">  938</span>&#160;                                                             kernel_h, kernel_size, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l00939"></a><span class="lineno">  939</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelW = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l00940"></a><span class="lineno">  940</span>&#160;                                                             kernel_w, kernel_size, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l00941"></a><span class="lineno">  941</span>&#160;</div><div class="line"><a name="l00942"></a><span class="lineno">  942</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideH = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l00943"></a><span class="lineno">  943</span>&#160;                                                             stride_h, stride, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 1u);</div><div class="line"><a name="l00944"></a><span class="lineno">  944</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideW = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l00945"></a><span class="lineno">  945</span>&#160;                                                             stride_w, stride, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 1u);</div><div class="line"><a name="l00946"></a><span class="lineno">  946</span>&#160;</div><div class="line"><a name="l00947"></a><span class="lineno">  947</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padH = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l00948"></a><span class="lineno">  948</span>&#160;                                                          pad_h, pad, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 0u);</div><div class="line"><a name="l00949"></a><span class="lineno">  949</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padW = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l00950"></a><span class="lineno">  950</span>&#160;                                                          pad_w, pad, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 0u);</div><div class="line"><a name="l00951"></a><span class="lineno">  951</span>&#160;</div><div class="line"><a name="l00952"></a><span class="lineno">  952</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dilationH = convParam.dilation_size() &gt; 0 ? convParam.dilation(0) : 1;</div><div class="line"><a name="l00953"></a><span class="lineno">  953</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dilationW = convParam.dilation_size() &gt; 1 ? convParam.dilation(1) :</div><div class="line"><a name="l00954"></a><span class="lineno">  954</span>&#160;        convParam.dilation_size() &gt; 0 ? convParam.dilation(0) : 1;</div><div class="line"><a name="l00955"></a><span class="lineno">  955</span>&#160;</div><div class="line"><a name="l00956"></a><span class="lineno">  956</span>&#160;    <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> convolution2dDescriptor;</div><div class="line"><a name="l00957"></a><span class="lineno">  957</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>     = padW;</div><div class="line"><a name="l00958"></a><span class="lineno">  958</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>    = padW;</div><div class="line"><a name="l00959"></a><span class="lineno">  959</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>      = padH;</div><div class="line"><a name="l00960"></a><span class="lineno">  960</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>   = padH;</div><div class="line"><a name="l00961"></a><span class="lineno">  961</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>     = strideW;</div><div class="line"><a name="l00962"></a><span class="lineno">  962</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>     = strideH;</div><div class="line"><a name="l00963"></a><span class="lineno">  963</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a>   = dilationW;</div><div class="line"><a name="l00964"></a><span class="lineno">  964</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a>   = dilationH;</div><div class="line"><a name="l00965"></a><span class="lineno">  965</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = convParam.has_bias_term() ? convParam.bias_term() : <span class="keyword">true</span>;</div><div class="line"><a name="l00966"></a><span class="lineno">  966</span>&#160;</div><div class="line"><a name="l00967"></a><span class="lineno">  967</span>&#160;    <span class="keywordflow">if</span> (numGroups &gt; numFilters)</div><div class="line"><a name="l00968"></a><span class="lineno">  968</span>&#160;    {</div><div class="line"><a name="l00969"></a><span class="lineno">  969</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00970"></a><span class="lineno">  970</span>&#160;            fmt::format(<span class="stringliteral">&quot;Error parsing Convolution: {}. &quot;</span></div><div class="line"><a name="l00971"></a><span class="lineno">  971</span>&#160;                        <span class="stringliteral">&quot;The &#39;group&#39;={} parameter cannot be larger than the &quot;</span></div><div class="line"><a name="l00972"></a><span class="lineno">  972</span>&#160;                        <span class="stringliteral">&quot;number of filters supplied =&#39;{}&#39;. {}&quot;</span>,</div><div class="line"><a name="l00973"></a><span class="lineno">  973</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l00974"></a><span class="lineno">  974</span>&#160;                        numGroups,</div><div class="line"><a name="l00975"></a><span class="lineno">  975</span>&#160;                        numFilters,</div><div class="line"><a name="l00976"></a><span class="lineno">  976</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00977"></a><span class="lineno">  977</span>&#160;    }</div><div class="line"><a name="l00978"></a><span class="lineno">  978</span>&#160;</div><div class="line"><a name="l00979"></a><span class="lineno">  979</span>&#160;    <span class="keywordflow">if</span> (inputShape.dim_size() != 4)</div><div class="line"><a name="l00980"></a><span class="lineno">  980</span>&#160;    {</div><div class="line"><a name="l00981"></a><span class="lineno">  981</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00982"></a><span class="lineno">  982</span>&#160;            fmt::format(<span class="stringliteral">&quot;Convolution input shape is expected to have 4 dimensions. &quot;</span></div><div class="line"><a name="l00983"></a><span class="lineno">  983</span>&#160;                        <span class="stringliteral">&quot;{}&#39;s input has only {}. {}&quot;</span>,</div><div class="line"><a name="l00984"></a><span class="lineno">  984</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l00985"></a><span class="lineno">  985</span>&#160;                        inputShape.dim_size(),</div><div class="line"><a name="l00986"></a><span class="lineno">  986</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00987"></a><span class="lineno">  987</span>&#160;    }</div><div class="line"><a name="l00988"></a><span class="lineno">  988</span>&#160;</div><div class="line"><a name="l00989"></a><span class="lineno">  989</span>&#160;    <span class="keywordflow">if</span> (numGroups &gt; 1)</div><div class="line"><a name="l00990"></a><span class="lineno">  990</span>&#160;    {</div><div class="line"><a name="l00991"></a><span class="lineno">  991</span>&#160;        <span class="keywordflow">if</span> (numGroups &gt; inputShape.dim(1))</div><div class="line"><a name="l00992"></a><span class="lineno">  992</span>&#160;        {</div><div class="line"><a name="l00993"></a><span class="lineno">  993</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00994"></a><span class="lineno">  994</span>&#160;                fmt::format(<span class="stringliteral">&quot;Error parsing Convolution: {}. &quot;</span></div><div class="line"><a name="l00995"></a><span class="lineno">  995</span>&#160;                            <span class="stringliteral">&quot;The &#39;group&#39;={} parameter cannot be larger than the &quot;</span></div><div class="line"><a name="l00996"></a><span class="lineno">  996</span>&#160;                            <span class="stringliteral">&quot;channel of the input shape={} (in NCHW format). {}&quot;</span>,</div><div class="line"><a name="l00997"></a><span class="lineno">  997</span>&#160;                            layerParam.name(),</div><div class="line"><a name="l00998"></a><span class="lineno">  998</span>&#160;                            numGroups,</div><div class="line"><a name="l00999"></a><span class="lineno">  999</span>&#160;                            inputShape.dim(1),</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>&#160;                            <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>&#160;        }</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span> (numGroups == inputShape.dim(1))</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>&#160;        {</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>&#160;            <span class="comment">// we use a depthwise convolution here, because the number of groups equals to the</span></div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>&#160;            <span class="comment">// input channels</span></div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>&#160;            <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#adcb87456482d5df17ef09eca1a808091">AddConvLayerWithDepthwiseConv</a>(layerParam, convolution2dDescriptor, kernelW, kernelH);</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>&#160;            <span class="keywordflow">return</span>;</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>&#160;        }</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>&#160;        <span class="keywordflow">else</span></div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>&#160;        {</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>&#160;            <span class="comment">// we split the input by channels into channels/groups separate convolutions</span></div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>&#160;            <span class="comment">// and concatenate the results afterwards</span></div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>&#160;            <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9fea304829fe514d664de515ca5c3918">AddConvLayerWithSplits</a>(layerParam, convolution2dDescriptor, kernelW, kernelH);</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>&#160;            <span class="keywordflow">return</span>;</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>&#160;        }</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>&#160;    }</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>&#160;</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>&#160; 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       3, (static_cast&lt;int&gt;(</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>&#160;                static_cast&lt;float&gt;(inputShape.dim(3) + 2 * padW - (dilationW * (kernelW - 1) + 1)) /</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>&#160;                static_cast&lt;float&gt;(strideW)) + 1));</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>&#160;</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>&#160;    <span class="comment">// Load the weight data for ALL groups</span></div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>&#160;    vector&lt;float&gt; weightData(armnn::numeric_cast&lt;size_t&gt;(inputShape.dim(1) *</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>&#160;                                                         outputShape.dim(1) *</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>&#160;                                                         kernelH *</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>&#160;                                                         kernelW));</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>&#160;    GetDataFromBlob(layerParam, weightData, 0);</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>&#160;</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightDimSizes[4] = {</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>&#160;        <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(outputShape.dim(1)), <span class="comment">// output channels</span></div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>&#160;        static_cast&lt;unsigned int&gt;(inputShape.dim(1)),  <span class="comment">// input channels</span></div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>&#160;        kernelH,</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>&#160;        kernelW};</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>&#160;</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* returnLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>&#160;</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>&#160;    <span class="comment">// Pull out the weights for this group from that loaded from the model file earlier</span></div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(4, weightDimSizes, DataType::Float32), weightData.data());</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>&#160;    <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a> optionalBiases;</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>&#160;    vector&lt;float&gt; biasData;</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>&#160;    <span class="keywordflow">if</span> (convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>&#160;    {</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo;</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>&#160;</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>&#160;        biasData.resize(armnn::numeric_cast&lt;size_t&gt;(outputShape.dim(1)), 1.f);</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>&#160;        GetDataFromBlob(layerParam, biasData, 1);</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>&#160;</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasDimSizes[1] = {<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(outputShape.dim(1))};</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>&#160;        biasInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, biasDimSizes, DataType::Float32);</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>&#160;</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>&#160;        <span class="comment">// Pull out the biases for this group from that loaded from the model file earlier</span></div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(biasInfo, biasData.data());</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>&#160;        optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a>(biases);</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>&#160;    }</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>&#160;    returnLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddConvolution2dLayer(convolution2dDescriptor,</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>&#160;                                                   weights,</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>&#160;                                                   optionalBiases,</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>&#160;                                                   layerParam.name().c_str());</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>&#160;</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>&#160;    <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>&amp; inputConnection = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0));</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>&#160;    inputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(returnLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>&#160;    returnLayer-&gt;GetOutputSlot(0).SetTensorInfo(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(outputShape));</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>&#160;</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>&#160;    <span class="keywordflow">if</span> (!returnLayer)</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>&#160;    {</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>&#160;            fmt::format(<span class="stringliteral">&quot;Failed to create Convolution layer. &quot;</span></div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>&#160;                        <span class="stringliteral">&quot;Layer={} #groups={} #filters={} {}&quot;</span>,</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>&#160;                        numGroups,</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>&#160;                        numFilters,</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>&#160;    }</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>&#160;</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), returnLayer-&gt;GetOutputSlot(0));</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>&#160;}</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>&#160;</div><div class="line"><a name="l01094"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac3f705f208b5ee9f540577524b2ad513"> 1094</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac3f705f208b5ee9f540577524b2ad513">ICaffeParser::CaffeParserImpl::ParseDeconvLayer</a>(<span class="keyword">const</span> LayerParameter&amp; layerParam)</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>&#160;{</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>&#160;    <span class="comment">// Ignored Caffe Parameters</span></div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>&#160;    <span class="comment">// * Weight Filler</span></div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>&#160;    <span class="comment">// * Bias Filler</span></div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>&#160;    <span class="comment">// * Engine</span></div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>&#160;    <span class="comment">// * Force nd_im2col</span></div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>&#160;    <span class="comment">// * Axis</span></div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>&#160;</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>&#160;    <span class="comment">// Not Available ArmNN Interface Parameters</span></div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>&#160;    <span class="comment">// * Rounding policy;</span></div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>&#160;</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>&#160;    <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layerParam.type() == <span class="stringliteral">&quot;Deconvolution&quot;</span>);</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>&#160;</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>&#160;    ConvolutionParameter convParam = layerParam.convolution_param();</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>&#160;    BlobShape inputShape = <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>());</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numGroups = convParam.has_group() ? convParam.group() : 1;</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numFilters = convParam.num_output();</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>&#160;</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> notFound = std::numeric_limits&lt;unsigned int&gt;::max();</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>&#160;</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelH = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>&#160;                                                             kernel_h, kernel_size, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelW = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>&#160;                                                             kernel_w, kernel_size, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>&#160;</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideH = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>&#160;                                                             stride_h, stride, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 1u);</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideW = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>&#160;                                                             stride_w, stride, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 1u);</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>&#160;</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padH = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>&#160;                                                          pad_h, pad, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 0u);</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padW = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>&#160;                                                          pad_w, pad, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 0u);</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>&#160;</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dilationH = convParam.dilation_size() &gt; 0 ? convParam.dilation(0) : 1;</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dilationW = convParam.dilation_size() &gt; 1 ? convParam.dilation(1) :</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>&#160;        convParam.dilation_size() &gt; 0 ? convParam.dilation(0) : 1;</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>&#160;</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>&#160;    <span class="keywordflow">if</span> (dilationH != 1 || dilationW != 1) {</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>&#160;        fmt::format(<span class="stringliteral">&quot;Dilated decnvolution is not supported. &quot;</span></div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>&#160;                <span class="stringliteral">&quot;{}&#39;s input has dilation {} {}. {}&quot;</span>,</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>&#160;                layerParam.name(),</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>&#160;                dilationW, dilationH,</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>&#160;                <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString());</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>&#160;    }</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>&#160;</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>&#160;    <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">TransposeConvolution2dDescriptor</a> deconvolution2dDescriptor;</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>&#160;    deconvolution2dDescriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>     = padW;</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>&#160;    deconvolution2dDescriptor.m_PadRight    = padW;</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>&#160;    deconvolution2dDescriptor.m_PadTop      = padH;</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>&#160;    deconvolution2dDescriptor.m_PadBottom   = padH;</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>&#160;    deconvolution2dDescriptor.m_StrideX     = strideW;</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>&#160;    deconvolution2dDescriptor.m_StrideY     = strideH;</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>&#160;    deconvolution2dDescriptor.m_BiasEnabled = convParam.has_bias_term() ? convParam.bias_term() : <span class="keyword">true</span>;</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>&#160;</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>&#160;    <span class="keywordflow">if</span> (numGroups &gt; numFilters)</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>&#160;    {</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>&#160;            fmt::format(<span class="stringliteral">&quot;Error parsing Deconvolution: {}. &quot;</span></div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>&#160;                        <span class="stringliteral">&quot;The &#39;group&#39;={} parameter cannot be larger than the &quot;</span></div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>&#160;                        <span class="stringliteral">&quot;number of filters supplied =&#39;{}&#39;. {}&quot;</span>,</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>&#160;                        numGroups,</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>&#160;                        numFilters,</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>&#160;    }</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>&#160;</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>&#160;    <span class="keywordflow">if</span> (inputShape.dim_size() != 4)</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>&#160;    {</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>&#160;            fmt::format(<span class="stringliteral">&quot;Deconvolution input shape is expected to have 4 dimensions. &quot;</span></div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>&#160;                        <span class="stringliteral">&quot;{}&#39;s input has only {}. {}&quot;</span>,</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>&#160;                        inputShape.dim_size(),</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>&#160;    }</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>&#160;</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>&#160;    <span class="keywordflow">if</span> (numGroups &gt; 1)</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>&#160;    {</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>&#160;        <span class="keywordflow">if</span> (numGroups &gt; inputShape.dim(1))</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>&#160;        {</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>&#160;                fmt::format(<span class="stringliteral">&quot;Error parsing Deconvolution: {}. &quot;</span></div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>&#160;                            <span class="stringliteral">&quot;The &#39;group&#39;={} parameter cannot be larger than the &quot;</span></div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>&#160;                            <span class="stringliteral">&quot;channel of the input shape={} (in NCHW format). {}&quot;</span>,</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>&#160;                            layerParam.name(),</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>&#160;                            numGroups,</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>&#160;                            inputShape.dim(1),</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>&#160;                            <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>&#160;        }</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>&#160;        <span class="keywordflow">else</span></div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>&#160;        {</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>&#160;            <span class="comment">// we split the input by channels into channels/groups separate convolutions</span></div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>&#160;            <span class="comment">// and concatenate the results afterwards</span></div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>&#160;            <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77f532a6a82afeb6e79957726a9517a5">AddDeconvLayerWithSplits</a>(layerParam, deconvolution2dDescriptor, kernelW, kernelH);</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>&#160;            <span class="keywordflow">return</span>;</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>&#160;        }</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>&#160;    }</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>&#160;</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>&#160;    <span class="comment">// NOTE: at this point we only need to handle #group=1 case, all other cases should be</span></div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>&#160;    <span class="comment">//       handled by the AddDeconvLayer* helpers</span></div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>&#160;</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>&#160;    <span class="comment">// Populate deconvolution output tensor descriptor dimensions</span></div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>&#160;    BlobShape outputShape;</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>&#160;    outputShape.add_dim(0);</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>&#160;    outputShape.set_dim(0, inputShape.dim(0));</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>&#160;    outputShape.add_dim(1);</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>&#160;    outputShape.set_dim(1, numFilters);</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>&#160;    outputShape.add_dim(2);</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>&#160;    outputShape.set_dim(</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>&#160;        2, (static_cast&lt;int&gt;(</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>&#160;                strideH * (inputShape.dim(2) - 1) - 2 * padH + (dilationH * (kernelH - 1) + 1))));</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>&#160;    outputShape.add_dim(3);</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>&#160;    outputShape.set_dim(</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>&#160;        3, (static_cast&lt;int&gt;(</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>&#160;                strideW * (inputShape.dim(3) - 1) - 2 * padW + (dilationW * (kernelW - 1) + 1))));</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>&#160;</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>&#160;    <span class="comment">// Load the weight data for ALL groups</span></div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>&#160;    vector&lt;float&gt; weightData(armnn::numeric_cast&lt;size_t&gt;(inputShape.dim(1) *</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>&#160;                                                         outputShape.dim(1) *</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>&#160;                                                         kernelH *</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>&#160;                                                         kernelW));</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>&#160;    GetDataFromBlob(layerParam, weightData, 0);</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>&#160;</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightDimSizes[4] = {</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>&#160;        <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(outputShape.dim(1)), <span class="comment">// output channels</span></div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>&#160;        static_cast&lt;unsigned int&gt;(inputShape.dim(1)),  <span class="comment">// input channels</span></div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>&#160;        kernelH,</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>&#160;        kernelW};</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>&#160;</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* returnLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>&#160;</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>&#160;    <span class="comment">// Pull out the weights for this group from that loaded from the model file earlier</span></div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(4, weightDimSizes, DataType::Float32), weightData.data());</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>&#160;    <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a> optionalBiases;</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>&#160;    vector&lt;float&gt; biasData;</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>&#160;    <span class="keywordflow">if</span> (deconvolution2dDescriptor.m_BiasEnabled)</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>&#160;    {</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo;</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>&#160;</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>&#160;        biasData.resize(armnn::numeric_cast&lt;size_t&gt;(outputShape.dim(1)), 1.f);</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>&#160;        GetDataFromBlob(layerParam, biasData, 1);</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>&#160;</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasDimSizes[1] = {<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(outputShape.dim(1))};</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>&#160;        biasInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, biasDimSizes, DataType::Float32);</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>&#160;</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>&#160;        <span class="comment">// Pull out the biases for this group from that loaded from the model file earlier</span></div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(biasInfo, biasData.data());</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>&#160;        optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a>(biases);</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>&#160;    }</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>&#160;    returnLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddTransposeConvolution2dLayer(deconvolution2dDescriptor,</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>&#160;                                                   weights,</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>&#160;                                                   optionalBiases,</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>&#160;                                                   layerParam.name().c_str());</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>&#160;</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>&#160;    <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>&amp; inputConnection = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0));</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>&#160;    inputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(returnLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>&#160;    returnLayer-&gt;GetOutputSlot(0).SetTensorInfo(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(outputShape));</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>&#160;</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>&#160;    <span class="keywordflow">if</span> (!returnLayer)</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>&#160;    {</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>&#160;            fmt::format(<span class="stringliteral">&quot;Failed to create Deconvolution layer. &quot;</span></div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>&#160;                        <span class="stringliteral">&quot;Layer={} #groups={} #filters={} {}&quot;</span>,</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>&#160;                        numGroups,</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>&#160;                        numFilters,</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>&#160;    }</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>&#160;</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), returnLayer-&gt;GetOutputSlot(0));</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>&#160;}</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>&#160;</div><div class="line"><a name="l01270"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab3329e4bcd8e42cd314f84c8260b06ad"> 1270</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab3329e4bcd8e42cd314f84c8260b06ad">ICaffeParser::CaffeParserImpl::ParsePoolingLayer</a>(<span class="keyword">const</span> LayerParameter&amp; layerParam)</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>&#160;{</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>&#160;    <span class="comment">// Ignored Caffe Parameters</span></div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>&#160;    <span class="comment">//      Stochastic Pooling</span></div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>&#160;    <span class="comment">//      Engine</span></div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>&#160;</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>&#160;    PoolingParameter param = layerParam.pooling_param();</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>&#160;</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> notFound = std::numeric_limits&lt;unsigned int&gt;::max();</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>&#160;</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_h = <a class="code" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a>(param, PoolingParameter,</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>&#160;                                                       kernel_h, kernel_size, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_w = <a class="code" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a>(param, PoolingParameter,</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>&#160;                                                       kernel_w, kernel_size, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>&#160;</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>&#160;    <span class="keywordflow">if</span> ((kernel_h == notFound || kernel_w == notFound) &amp;&amp; param.has_global_pooling())</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>&#160;    {</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>&#160;        kernel_h = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>&#160;        kernel_w = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3];</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>&#160;    }</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>&#160;</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_h = <a class="code" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a>(param, PoolingParameter,</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>&#160;                                                       stride_h, stride, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_w = <a class="code" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a>(param, PoolingParameter,</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>&#160;                                                       stride_h, stride, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>&#160;</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>&#160;    <span class="keywordflow">if</span> ((stride_h == notFound || stride_w == notFound) &amp;&amp; param.has_global_pooling())</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>&#160;    {</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>&#160;        stride_h = 1;</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>&#160;        stride_w = 1;</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>&#160;    }</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>&#160;</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pad_h = <a class="code" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a>(param, PoolingParameter,</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>&#160;                                                    pad_h, pad, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 0u);</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pad_w = <a class="code" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a>(param, PoolingParameter,</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>&#160;                                                    pad_w, pad, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 0u);</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>&#160;</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>&#160;    <span class="comment">// Populate Weight and Bias Filter Descriptor</span></div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>&#160;    <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>&#160;    <span class="keywordflow">if</span> (param.has_pool())</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>&#160;    {</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>&#160;        PoolingParameter_PoolMethod p = param.pool();</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>&#160;        <span class="keywordflow">switch</span> (p)</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>&#160;        {</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>&#160;            <span class="keywordflow">case</span> PoolingParameter_PoolMethod_MAX:</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>&#160;            {</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>&#160;                pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>&#160;                <span class="keywordflow">break</span>;</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>&#160;            }</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>&#160;            <span class="keywordflow">case</span> PoolingParameter_PoolMethod_AVE:</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>&#160;            {</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>&#160;                pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = PoolingAlgorithm::Average;</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>&#160;                <span class="keywordflow">break</span>;</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>&#160;            }</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>&#160;            <span class="keywordflow">case</span> PoolingParameter_PoolMethod_STOCHASTIC:</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>&#160;            {</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>&#160;                <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>&#160;                    fmt::format(<span class="stringliteral">&quot;Pooling Layer: Stochastic Pooling Not Supported. Layer={} {}&quot;</span>,</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>&#160;                                layerParam.name(),</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>&#160;                                <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>&#160;            }</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>&#160;            <span class="keywordflow">default</span>:</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>&#160;            {</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>&#160;                <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>&#160;                    fmt::format(<span class="stringliteral">&quot;Pooling Layer: unknown pooling method: {} for layer: {} {}&quot;</span>,</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>&#160;                                p,</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>&#160;                                layerParam.name(),</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>&#160;                                <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>&#160;            }</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>&#160;        }</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>&#160;    }</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>&#160;    {</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>&#160;            fmt::format(<span class="stringliteral">&quot;No Pooling Method Defined for {} {}&quot;</span>,</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>&#160;    }</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>&#160;</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>     = pad_w;</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>    = pad_w;</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>      = pad_h;</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>   = pad_h;</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>     = stride_w;</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>     = stride_h;</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>   = kernel_w;</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a>  = kernel_h;</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>&#160;</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = OutputShapeRounding::Ceiling;</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a>  = PaddingMethod::IgnoreValue;</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>&#160;</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* poolingLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddPooling2dLayer(pooling2dDescriptor,</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>&#160;        layerParam.name().c_str());</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>&#160;</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>&#160;        { inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0],</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>&#160;          inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1],</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>&#160;          <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(ceil(</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>&#160;              static_cast&lt;float&gt;(inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2] + 2 * pad_h - kernel_h) /</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>&#160;              armnn::numeric_cast&lt;float&gt;(stride_h))) + 1,</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>&#160;          <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(ceil(</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>&#160;              static_cast&lt;float&gt;(inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3] + 2 * pad_w - kernel_w) /</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>&#160;              armnn::numeric_cast&lt;float&gt;(stride_w))) + 1 },</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>&#160;        DataType::Float32);</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>&#160;</div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(poolingLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>&#160;    poolingLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), poolingLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>&#160;}</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>&#160;</div><div class="line"><a name="l01382"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5ce905b7412e68d588e08f4afc333aac"> 1382</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5ce905b7412e68d588e08f4afc333aac">ICaffeParser::CaffeParserImpl::ParseArgmaxLayer</a>(<span class="keyword">const</span> LayerParameter&amp; layerParam)</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>&#160;{</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>&#160;    ArgMaxParameter param = layerParam.argmax_param();</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>&#160;</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>&#160;    BlobShape inputShape = <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>());</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>&#160;</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>&#160; 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Layer={} {}&quot;</span>,</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>&#160;                    layerParam.name(),</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>&#160;                    <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>&#160;    }</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>&#160;</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outMaxVal = param.has_out_max_val() ? param.out_max_val() : <span class="keyword">false</span>;</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>&#160;    <span class="keywordflow">if</span> (outMaxVal) {</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>&#160;                fmt::format(<span class="stringliteral">&quot;ArgMaxLayer: Does not support out_max_val. Layer={} {}&quot;</span>,</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>&#160;                    layerParam.name(),</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>&#160;                    <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>&#160;    }</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>&#160;</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>&#160;    <span class="keywordtype">int</span> axis = param.has_axis() ? param.axis() : 1;</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>&#160;    <span class="keywordflow">if</span> (axis &lt; 0) {</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>&#160;        axis = inputShape.dim_size() - axis;</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>&#160; 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                       inputShape.dim_size(),</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>&#160;                        axis,</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>&#160;    }</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>&#160;</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>&#160;    <a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">ArgMinMaxDescriptor</a> desc;</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>&#160;    desc.<a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml#a214c3636fdf0ea5bac8edb42d0e6c7f0">m_Axis</a> = axis;</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>&#160;    desc.<a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml#abce784834696eb928c620f1fafe71a8d">m_Output_Type</a> = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>;</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>&#160;    desc.<a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">ArgMinMaxFunction::Max</a>;</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>&#160;</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* argmaxLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddArgMinMaxLayer(desc,</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>&#160;        layerParam.name().c_str());</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>&#160;</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape(static_cast&lt;unsigned int&gt;(inputShape.dim_size() - 1));</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>&#160;    <span class="keywordtype">int</span> j = 0;</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>&#160;    <span class="comment">// remove the flatten axis</span></div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; inputShape.dim_size(); ++i)</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>&#160;    {</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>&#160; 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   argmaxLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), argmaxLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>&#160;}</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>&#160;</div><div class="line"><a name="l01442"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a8449e66d395c0525561e3c67b100bafe"> 1442</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a8449e66d395c0525561e3c67b100bafe">ICaffeParser::CaffeParserImpl::ParseReluLayer</a>(<span class="keyword">const</span> LayerParameter&amp; layerParam)</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>&#160;{</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>&#160;</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">string</span>&amp; name = layerParam.name();</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>&#160;    <span class="keyword">const</span> ReLUParameter&amp; param = layerParam.relu_param();</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>&#160;</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>&#160;    <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span> negativeSlope = param.negative_slope();</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>&#160;    <span class="keywordflow">if</span> (negativeSlope == 0.0f)</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>&#160;    {</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>&#160;        activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::ReLu;</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>&#160;    }</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>&#160;    {</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>&#160;        activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::LeakyReLu;</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>&#160; 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layerParam)</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>&#160;{</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>&#160;</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>&#160;    LRNParameter param = layerParam.lrn_param();</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>&#160;</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>&#160;</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>&#160; 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   {</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>&#160;        normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">m_Alpha</a> = param.alpha();</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>&#160;        normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">m_Alpha</a> /= <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a>&lt;<span class="keywordtype">float</span>&gt;(param.local_size());</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>&#160;    }</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>&#160;    {</div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>&#160;            fmt::format(<span class="stringliteral">&quot;Alpha not defined for LRN layer {} {}&quot;</span>,</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>&#160;    }</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>&#160;    <span class="keywordflow">if</span> (param.has_beta())</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>&#160;    {</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>&#160;        normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = param.beta();</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>&#160;    }</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>&#160;    {</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>&#160;            fmt::format(<span class="stringliteral">&quot;Beta not defined for LRN layer {} {}&quot;</span>,</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>&#160;    }</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>&#160;</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>&#160;    <span class="keywordflow">if</span> (param.has_k())</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>&#160;    {</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>&#160;        normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">m_K</a> = param.k();</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>&#160;    }</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>&#160;    {</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>&#160;        normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">m_K</a> = 1;</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>&#160;    }</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>&#160;</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> normLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddNormalizationLayer(normalizationDescriptor,</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>&#160;        layerParam.name().c_str());</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(normLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>&#160;    normLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>&#160;</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), normLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>&#160;}</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>&#160;</div><div class="line"><a name="l01567"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a34f6df4b84de1e269bcf02efeecc3892"> 1567</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a34f6df4b84de1e269bcf02efeecc3892">ICaffeParser::CaffeParserImpl::ParseInnerProductLayer</a>(<span class="keyword">const</span> LayerParameter&amp; layerParam)</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>&#160;{</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>&#160;    InnerProductParameter param = layerParam.inner_product_param();</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>&#160;</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>&#160;</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = param.num_output();</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>&#160;</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>&#160;    <span class="comment">// Ignored Caffe Parameters:</span></div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>&#160;    <span class="comment">// Weight Filler</span></div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>&#160;    <span class="comment">// Bias Filler</span></div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>&#160;    <span class="comment">// Engine</span></div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>&#160;    <span class="comment">// Axis</span></div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>&#160;</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>&#160;    <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> tensorFullyConnectedDescriptor;</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>&#160;</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>&#160;    <span class="keywordflow">if</span> (param.has_transpose())</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>&#160;    {</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>&#160;        <span class="comment">// If true, assumes transposed weights.</span></div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>&#160;        tensorFullyConnectedDescriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = param.transpose();</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>&#160;    }</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>&#160;    {</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>&#160;        <span class="comment">// Caffe defaults to transposed.</span></div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>&#160;        tensorFullyConnectedDescriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>&#160;    }</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>&#160;</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>&#160;</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightInfo;</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo;</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>&#160;</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>&#160; 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   }</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>&#160;</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span>* weightDataPtr = GetArrayPtrFromBlob(layerParam, 0);</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> swTD[2] = { outputSize, inputSize };</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(2, swTD, DataType::Float32), weightDataPtr);</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>&#160;</div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>&#160;    tensorFullyConnectedDescriptor.<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="l01611"></a><span class="lineno"> 1611</span>&#160;    <span class="comment">// Todo: check whether bias enabled.</span></div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* fullyConnectedLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span>&#160;    <span class="keywordflow">if</span> (tensorFullyConnectedDescriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>&#160;    {</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>&#160;        <span class="comment">// BIAS VALUE</span></div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">float</span>* biasDataPtr = GetArrayPtrFromBlob(layerParam, 1);</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>&#160;</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> sbTD[1] = { outputSize };</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>&#160;</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, sbTD, DataType::Float32), biasDataPtr);</div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span>&#160;</div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>&#160;        fullyConnectedLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddFullyConnectedLayer(tensorFullyConnectedDescriptor,</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>&#160;                                                                weights,</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>&#160;                                                                <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a>(biases),</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>&#160;                                                                layerParam.name().c_str());</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>&#160;    }</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>&#160;    {</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>&#160;        fullyConnectedLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddFullyConnectedLayer(tensorFullyConnectedDescriptor,</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>&#160;                                                                weights,</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>&#160;                                                                <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>&#160;                                                                layerParam.name().c_str());</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>&#160;    }</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>&#160;</div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo({ inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0], outputSize }, DataType::Float32);</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(fullyConnectedLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>&#160;    fullyConnectedLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), fullyConnectedLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>&#160;}</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>&#160;</div><div class="line"><a name="l01641"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1c0594bf03dfbb44029465d3466127b3"> 1641</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1c0594bf03dfbb44029465d3466127b3">ICaffeParser::CaffeParserImpl::ParseSoftmaxLayer</a>(<span class="keyword">const</span> LayerParameter&amp; layerParam)</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>&#160;{</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>&#160;</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>&#160;    SoftmaxParameter param = layerParam.softmax_param();</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>&#160;</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>&#160;</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>&#160;    <span class="comment">// Ignored Caffe Parameters:</span></div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>&#160;    <span class="comment">//      axis</span></div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>&#160;    <span class="comment">//      Engine</span></div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>&#160;</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>&#160;    <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a> softmaxDescriptor;</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>&#160;    softmaxDescriptor.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a214c3636fdf0ea5bac8edb42d0e6c7f0">m_Axis</a> = 1;</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> softmaxLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddSoftmaxLayer(</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>&#160;        softmaxDescriptor,</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>&#160;        layerParam.name().c_str());</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(softmaxLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>&#160;    softmaxLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), softmaxLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>&#160;}</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span>&#160;</div><div class="line"><a name="l01663"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a99a846a21b3a6ec97cc1d4344b91df36"> 1663</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a99a846a21b3a6ec97cc1d4344b91df36">ICaffeParser::CaffeParserImpl::ParseEltwiseLayer</a>(<span class="keyword">const</span> LayerParameter&amp; layerParam)</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>&#160;{</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>&#160;    ValidateNumInputsOutputs(layerParam, 2, 1);</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>&#160;</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>&#160;</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>&#160;    <span class="comment">// Ignored Caffe Parameters:</span></div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>&#160;    <span class="comment">//      coeff</span></div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>&#160;</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>&#160;    EltwiseParameter_EltwiseOp operation = EltwiseParameter_EltwiseOp_SUM; <span class="comment">// Defaults to sum as per caffe.</span></div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>&#160;</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>&#160;    <span class="keywordflow">if</span> (layerParam.has_eltwise_param() &amp;&amp; layerParam.eltwise_param().has_operation())</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>&#160;    {</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>&#160;        operation = layerParam.eltwise_param().operation();</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>&#160;    }</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>&#160;</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* newLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>&#160;    <span class="keywordflow">switch</span> (operation)</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>&#160;    {</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>&#160;        <span class="keywordflow">case</span> EltwiseParameter_EltwiseOp_SUM:</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>&#160;        {</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>&#160;            newLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddAdditionLayer(layerParam.name().c_str());</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>&#160;        }</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>&#160;        <span class="keywordflow">case</span> EltwiseParameter_EltwiseOp_PROD:</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>&#160;        {</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>&#160;            newLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddMultiplicationLayer(layerParam.name().c_str());</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>&#160;        }</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>&#160;        <span class="keywordflow">default</span>:</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>&#160;        {</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>&#160;                fmt::format(<span class="stringliteral">&quot;Unsupported operation {} in Eltwise layer {} {}&quot;</span>,</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>&#160;                            operation,</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>&#160;                            layerParam.name(),</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>&#160;                            <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>&#160;        }</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>&#160;    }</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>&#160;</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(newLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(1)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(newLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>&#160;    newLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), newLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>&#160;}</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>&#160;</div><div class="line"><a name="l01708"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa0f0ff1cae05c1a0d7cc11b498714312"> 1708</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa0f0ff1cae05c1a0d7cc11b498714312">ICaffeParser::CaffeParserImpl::ParseConcatLayer</a>(<span class="keyword">const</span> LayerParameter&amp; layerParam)</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>&#160;{</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputs = <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(layerParam.bottom_size());</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>&#160;    <span class="comment">// We assume concat happens along the channel dimension, which is 1 in (0, 1, 2, 3).</span></div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDim = 1;</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numOfDims = 4;</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>&#160;</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>&#160;    <span class="comment">// we only consider 4-D tensor here</span></div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>&#160;    <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> concatDescriptor(static_cast&lt;uint32_t&gt;(numInputs), numOfDims);</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>&#160;    std::vector&lt;unsigned int&gt;mergeDimSizes(numOfDims, 0u);</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>&#160;</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mergeDim = 0;</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> viewIndex = 0; viewIndex &lt; numInputs; ++viewIndex)</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>&#160;    {</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>&#160;            layerParam.bottom(armnn::numeric_cast&lt;int&gt;(viewIndex))).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>&#160;        <span class="comment">// Checks whether the dimensions of the input tensors are actually 4.</span></div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>&#160;        <span class="keywordflow">if</span> (inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()!=4)</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>&#160;        {</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>&#160;                fmt::format(<span class="stringliteral">&quot;The number of dimensions for input tensors of &quot;</span></div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>&#160;                            <span class="stringliteral">&quot;the concatenation op should be 4. 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       mergeDim += mergeDimSizes[concatDim];</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>&#160;</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = concatDim+1; j &lt; numOfDims; ++j)</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>&#160;        {</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>&#160;            concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(viewIndex, j, 0);</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>&#160;        }</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>&#160;    }</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>&#160;    mergeDimSizes[concatDim] = mergeDim;</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>&#160;</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>&#160; 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       outputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatlayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(i));</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>&#160;    }</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>&#160;</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>&#160;    concatlayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(numOfDims, mergeDimSizes.data(), DataType::Float32));</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), concatlayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>&#160;}</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>&#160;</div><div class="line"><a name="l01767"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a940483591995bb812cfcd1595dba83c3"> 1767</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a940483591995bb812cfcd1595dba83c3">ICaffeParser::CaffeParserImpl::ParseBatchNormLayer</a>(<span class="keyword">const</span> LayerParameter&amp; layerParam)</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>&#160;{</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>&#160;</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>&#160;</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>&#160;    <span class="keywordtype">string</span> name = layerParam.name();</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>&#160;</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>&#160;    BatchNormParameter param = layerParam.batch_norm_param();</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>&#160;    <span class="comment">// If use_global_stats is not explicitly set in the model, assume it to be true (its default value</span></div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>&#160;    <span class="comment">// when the network is in the testing phase).</span></div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>&#160;    <span class="keywordflow">if</span> (param.has_use_global_stats())</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>&#160;    {</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>&#160;        <span class="keywordflow">if</span> (!param.use_global_stats())</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>&#160;        {</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>&#160;                fmt::format(<span class="stringliteral">&quot;Error parsing Batch Norm layer &#39;{}&#39;: &quot;</span></div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>&#160;                            <span class="stringliteral">&quot;Parameter &#39;use_global_stats&#39; is set to false, which is &quot;</span></div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>&#160;                            <span class="stringliteral">&quot;unsupported (value used for training). {}&quot;</span>,</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>&#160;                            name,</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>&#160;                            <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>&#160;        }</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>&#160;    }</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>&#160;</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>&#160;    <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> desc;</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>&#160;    desc.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = param.eps();</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>&#160;</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[]  = {channels};</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>&#160;</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>&#160;    vector&lt;float&gt; meanData(channels);</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>&#160;    GetDataFromBlob(layerParam, meanData, 0);</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>&#160;</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>&#160;    vector&lt;float&gt; varianceData(channels);</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>&#160;    GetDataFromBlob(layerParam, varianceData, 1);</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>&#160;</div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>&#160;    <span class="comment">// Reads moving average factor and applies scaling (if required).</span></div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>&#160;    <span class="keyword">const</span> BlobProto&amp; blob = layerParam.blobs(armnn::numeric_cast&lt;int&gt;(2));</div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span> movingAverageFactor = blob.data(armnn::numeric_cast&lt;int&gt;(0));</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>&#160;    <span class="keywordflow">if</span>(movingAverageFactor != 0.0f)</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>&#160;    {</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">float</span> scaleFactor = 1.0f / movingAverageFactor;</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>&#160;        <span class="keyword">auto</span> scaleFunction = [scaleFactor](<span class="keywordtype">float</span> f) -&gt; <span class="keywordtype">float</span> { <span class="keywordflow">return</span> f * scaleFactor; 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gammaData(channels, 1.0f);</div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>&#160;</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> mean(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), meanData);</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> variance(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), varianceData);</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> beta(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), betaData);</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> gamma(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), gammaData);</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>&#160;</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> batchNormLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddBatchNormalizationLayer(desc,</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>&#160; 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layerParam)</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>&#160;{</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>&#160;    <span class="comment">// Current unoptimal solution: add a batchnormalization layer with 0 mean and 1 variance.</span></div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>&#160;    ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>&#160;</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>&#160;</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>&#160; 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           fmt::format(<span class="stringliteral">&quot;Loading Scale Layer: Only axis 1 is supported currently. &quot;</span></div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>&#160;                        <span class="stringliteral">&quot;Layer={} Axis={} {}&quot;</span>,</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>&#160;                        param.axis(),</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>&#160;    }</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>&#160;</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>     channels = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>     shape[]  = {channels};</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>&#160;</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>&#160;    <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> desc;</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>&#160;    desc.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = 0.0f; <span class="comment">// Don&#39;t need epsilon if variance is 1.</span></div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>&#160;    vector&lt;float&gt; meanData(channels, 0.0f);</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>&#160;    vector&lt;float&gt; varianceData(channels, 1.0f);</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>&#160;    vector&lt;float&gt; betaData(channels, 0.0f);</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>&#160;    vector&lt;float&gt; gammaData(channels);</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>&#160;</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>&#160;    GetDataFromBlob(layerParam, gammaData, 0);</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>&#160;</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>&#160;    <span class="keywordflow">if</span>(param.has_bias_term())</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>&#160;    {</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>&#160;        GetDataFromBlob(layerParam, betaData, 1);</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>&#160;    }</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>&#160;</div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> mean(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), meanData);</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> variance(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), varianceData);</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> beta(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), betaData);</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> gamma(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), gammaData);</div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>&#160;</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> batchNormLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddBatchNormalizationLayer(desc,</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>&#160;        mean, variance, beta, gamma, name.c_str());</div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(batchNormLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>&#160;    batchNormLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), batchNormLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>&#160;}</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>&#160;</div><div class="line"><a name="l01881"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a3311e9dc3436fe83ef22c5f530fd3234"> 1881</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a3311e9dc3436fe83ef22c5f530fd3234">ICaffeParser::CaffeParserImpl::ParseSplitLayer</a>(<span class="keyword">const</span> caffe::LayerParameter&amp; layerParam)</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>&#160;{</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span>&#160;    <span class="comment">// Used in caffe to duplicate memory - not necessary in armnn.</span></div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>&#160;    <span class="keywordflow">if</span> (layerParam.bottom_size() != 1)</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>&#160;    {</div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>&#160;            fmt::format(<span class="stringliteral">&quot;Split layer &#39;{}&#39; should have exactly 1 bottom. &quot;</span></div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>&#160;                        <span class="stringliteral">&quot;#bottoms={} {}&quot;</span>,</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>&#160;                        layerParam.bottom_size(),</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>&#160;    }</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>&#160;    <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>&amp; outputSlot = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0));</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; layerParam.top_size(); i++)</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>&#160;    {</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>&#160;        <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(i), outputSlot);</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>&#160;    }</div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>&#160;}</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>&#160;</div><div class="line"><a name="l01900"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa4c22681675806fa2c5fbf403d49c628"> 1900</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa4c22681675806fa2c5fbf403d49c628">ICaffeParser::CaffeParserImpl::ParseDropoutLayer</a>(<span class="keyword">const</span> caffe::LayerParameter&amp; layerParam)</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>&#160;{</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>&#160;    <span class="comment">// Ignored for inference, so patch the single input to its single output.</span></div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>&#160;    <span class="keywordflow">if</span> (layerParam.bottom_size() != 1 || layerParam.top_size() != 1)</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>&#160;    {</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>&#160;            fmt::format(<span class="stringliteral">&quot;Dropout layer &#39;{}&#39; should have exactly 1 bottom and 1 top. &quot;</span></div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>&#160;                        <span class="stringliteral">&quot;#bottoms={} #tops={} {}&quot;</span>,</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>&#160;                        layerParam.name(),</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>&#160;                        layerParam.bottom_size(),</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>&#160;                        layerParam.top_size(),</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>&#160;    }</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)));</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>&#160;}</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>&#160;</div><div class="line"><a name="l01916"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2a1112c66d08e3760ecccf39c7854a90"> 1916</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2a1112c66d08e3760ecccf39c7854a90">ICaffeParser::CaffeParserImpl::TrackInputBinding</a>(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>,</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&amp; tensorInfo)</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>&#160;{</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5f5e6255b21fdf458d3733bbdcdc4af5">TrackBindingPoint</a>(layer, <span class="keywordtype">id</span>, tensorInfo, layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>(), <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac62e2558c14e01605f2b4e1e21cdd1e8">m_NetworkInputsBindingInfo</a>);</div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>&#160;}</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>&#160;</div><div class="line"><a name="l01923"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0c98e07875a82c71c65bbb53eb347561"> 1923</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0c98e07875a82c71c65bbb53eb347561">ICaffeParser::CaffeParserImpl::TrackOutputBinding</a>(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>,</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&amp; tensorInfo)</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>&#160;{</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5f5e6255b21fdf458d3733bbdcdc4af5">TrackBindingPoint</a>(layer, <span class="keywordtype">id</span>, tensorInfo, layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>(), <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a62d6d6cba9ed0d3ad63fffb40aec86b5">m_NetworkOutputsBindingInfo</a>);</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>&#160;}</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>&#160;</div><div class="line"><a name="l01930"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5f5e6255b21fdf458d3733bbdcdc4af5"> 1930</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5f5e6255b21fdf458d3733bbdcdc4af5">ICaffeParser::CaffeParserImpl::TrackBindingPoint</a>(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>,</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&amp; tensorInfo,</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">char</span>* bindingPointDesc,</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>&#160;    std::unordered_map&lt;std::string, BindingPointInfo&gt;&amp; nameToBindingInfo)</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>&#160;{</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>&#160;    <span class="keyword">const</span> std::string layerName = layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>();</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>&#160;    <span class="keyword">auto</span> it = nameToBindingInfo.find(layerName);</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>&#160;    <span class="keywordflow">if</span> (it == nameToBindingInfo.end())</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>&#160;    {</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>&#160;        nameToBindingInfo[layerName] = std::make_pair(<span class="keywordtype">id</span>, tensorInfo);</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>&#160;    }</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>&#160;    {</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>&#160;            fmt::format(<span class="stringliteral">&quot;Id {} used by more than one {} layer {}&quot;</span>,</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>&#160;                        <span class="keywordtype">id</span>,</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>&#160;                        bindingPointDesc,</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>&#160;    }</div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>&#160;}</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>&#160;</div><div class="line"><a name="l01952"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06"> 1952</a></span>&#160;<a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>&amp; <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">ICaffeParser::CaffeParserImpl::GetArmnnOutputSlotForCaffeTop</a>(<span class="keyword">const</span> std::string&amp; caffeTopName)<span class="keyword"> const</span></div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>&#160;    <span class="keyword">auto</span> it = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">m_ArmnnOutputSlotForCaffeTop</a>.find(caffeTopName);</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>&#160;    <span class="keywordflow">if</span> (it != <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">m_ArmnnOutputSlotForCaffeTop</a>.end())</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>&#160;    {</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>&#160;        <span class="keywordflow">return</span> *it-&gt;second;</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>&#160;    }</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>&#160;    {</div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>&#160;            fmt::format(<span class="stringliteral">&quot;Could not find armnn output slot for Caffe top &#39;{}&#39; {}&quot;</span>,</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>&#160;                        caffeTopName,</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>&#160;    }</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>&#160;}</div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>&#160;</div><div class="line"><a name="l01968"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa"> 1968</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">ICaffeParser::CaffeParserImpl::SetArmnnOutputSlotForCaffeTop</a>(</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>&#160;    <span class="keyword">const</span> std::string&amp; caffeTopName, <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>&amp; armnnOutputSlot)</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>&#160;{</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>&#160;    <span class="keyword">auto</span> it = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">m_ArmnnOutputSlotForCaffeTop</a>.find(caffeTopName);</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>&#160;    <span class="keywordflow">if</span> (it == <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">m_ArmnnOutputSlotForCaffeTop</a>.end())</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>&#160;    {</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>&#160;        <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">m_ArmnnOutputSlotForCaffeTop</a>[caffeTopName] = &amp;armnnOutputSlot;</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>&#160;    }</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>&#160;    {</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>&#160;            fmt::format(<span class="stringliteral">&quot;Attempting to add duplicate entry for Caffe top &#39;{}&#39; {}&quot;</span>,</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>&#160;                        caffeTopName,</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>&#160;    }</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>&#160;}</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>&#160;</div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>&#160;<span class="comment">// Note: can move to CaffeParser when/if we optimise the text/string format</span></div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>&#160;<span class="comment">//       to load on a layer by layer basis</span></div><div class="line"><a name="l01987"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a89631aa06b5c628c46674c202b40dbc5"> 1987</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a89631aa06b5c628c46674c202b40dbc5">ICaffeParser::CaffeParserImpl::ResolveInPlaceLayers</a>(caffe::NetParameter&amp; netParameter)</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>&#160;{</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>&#160;    <span class="comment">// Finds layers with the same top.</span></div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>&#160;    std::map&lt;std::string, std::vector&lt;caffe::LayerParameter*&gt;&gt; layersByTop;</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> layerIdx = 0; layerIdx &lt; netParameter.layer_size(); ++layerIdx)</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>&#160;    {</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>&#160;        caffe::LayerParameter&amp; layer = *netParameter.mutable_layer(layerIdx);</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>&#160;        std::string name = layer.name();</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; layer.top_size(); ++i)</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>&#160;        {</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>&#160;            layersByTop[layer.top(i)].push_back(&amp;layer);</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>&#160;        }</div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>&#160;    }</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>&#160;</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>&#160;    <span class="comment">// For each set of layers with the same top, resolves them to a linear chain rather than in-place layers.</span></div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>&#160;    <span class="comment">// Note that for &#39;regular&#39; layers, there will be a single layer in each group and so this will be a no-op.</span></div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">auto</span> layersWithSameTopIt : layersByTop)</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>&#160;    {</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>&#160;        <span class="keyword">const</span> std::string&amp; top = layersWithSameTopIt.first;</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>&#160;        <span class="keyword">const</span> std::vector&lt;caffe::LayerParameter*&gt;&amp; layersWithSameTop = layersWithSameTopIt.second;</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>&#160;</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>&#160;        <span class="comment">// Chains the layers together in the order that they are listed in the prototxt (hopefully this is correct).</span></div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>&#160;        <span class="comment">// Note that the last layer will not have its top modified so that other layers will continue to reference it.</span></div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> layerIdx = 0; layerIdx &lt; layersWithSameTop.size() - 1; ++layerIdx)</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>&#160;        {</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>&#160;            caffe::LayerParameter&amp; layer1 = *layersWithSameTop[layerIdx];</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>&#160;            caffe::LayerParameter&amp; layer2 = *layersWithSameTop[layerIdx+1];</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>&#160;            <span class="keywordflow">if</span> (layer1.top_size() != 1)</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>&#160;            {</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>&#160;                <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>&#160;                    fmt::format(<span class="stringliteral">&quot;Node &#39;{}&#39; is an in-place layer but doesn&#39;t have exactly one &quot;</span></div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>&#160;                                <span class="stringliteral">&quot;top. It has {} instead. {}&quot;</span>,</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>&#160;                                layer1.name(),</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>&#160;                                layer1.top_size(),</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>&#160;                                <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>&#160;            }</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>&#160;            std::string newTop = layer1.name() + <span class="stringliteral">&quot;_top&quot;</span>;</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>&#160;            layer1.set_top(0, newTop);</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>&#160;            <span class="keywordflow">if</span> (layer2.bottom_size() != 1 || layer2.bottom(0) != top)</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>&#160;            {</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>&#160;                <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>&#160;                    fmt::format(<span class="stringliteral">&quot;Node &#39;{}&#39; is an in-place layer but &quot;</span></div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>&#160;                                <span class="stringliteral">&quot;doesn&#39;t have exactly one bottom, or it doesn&#39;t match its top. &quot;</span></div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>&#160;                                <span class="stringliteral">&quot;#bottoms={}, first bottom is {}, top is {} {}&quot;</span>,</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>&#160;                                layer2.name(),</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>&#160;                                layer2.bottom(0),</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>&#160;                                top,</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>&#160;                                <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>&#160;            }</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>&#160;            layer2.set_bottom(0, newTop);</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>&#160;        }</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>&#160;    }</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>&#160;}</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>&#160;</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span>&#160;<span class="comment">// Note: can move to CaffeParser when/if we optimise the text/string format</span></div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>&#160;<span class="comment">//       to load on a layer by layer basis</span></div><div class="line"><a name="l02043"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c5eed5d48d21a8b7e3bcd2cab519217"> 2043</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c5eed5d48d21a8b7e3bcd2cab519217">ICaffeParser::CaffeParserImpl::LoadNetParam</a>(NetParameter&amp; netParameter)</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>&#160;{</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>&#160;    <span class="comment">// Caffe models sometimes have an implicit input layer.</span></div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>&#160;    <span class="comment">// In that case, add an explicit one.</span></div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>&#160;    <span class="keywordflow">if</span> (netParameter.input_size() &gt; 0)</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>&#160;    {</div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>&#160;        LayerParameter* newLayer = netParameter.add_layer();</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>&#160;</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>&#160;        newLayer-&gt;set_type(<span class="stringliteral">&quot;Input&quot;</span>);</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>&#160;        newLayer-&gt;set_name(netParameter.input(0));</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>&#160;        newLayer-&gt;add_top(netParameter.input(0));</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>&#160;</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>&#160;        InputParameter* inputParam = newLayer-&gt;mutable_input_param();</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>&#160;        BlobShape* shape = inputParam-&gt;add_shape();</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>&#160;</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>&#160;        <span class="keywordtype">int</span> dim_size = netParameter.input_dim_size();</div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; dim_size; ++i)</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>&#160;        {</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>&#160;            shape-&gt;add_dim(netParameter.input_dim(i));</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span>&#160;        }</div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span>&#160;    }</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span>&#160;</div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>&#160;    <span class="comment">// Replaces in-place layers with regular ones to make the rest of the parsing easier.</span></div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a89631aa06b5c628c46674c202b40dbc5">ResolveInPlaceLayers</a>(netParameter);</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>&#160;</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span>&#160;    <span class="comment">// Creates a lookup of Caffe layers by name.</span></div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; netParameter.layer_size(); ++i)</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>&#160;    {</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>&#160;        <span class="keyword">const</span> caffe::LayerParameter&amp; layer = netParameter.layer(i);</div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; layer.top_size(); ++i)</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>&#160;        {</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>&#160;            <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1424f1bfbfc81d317b51053bbb315ef1">m_CaffeLayersByTopName</a>[layer.top(i)] = &amp;layer;</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>&#160;        }</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>&#160;    }</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>&#160;</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>&#160;    <span class="comment">// Finds the output layers the user requested.</span></div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>&#160;    std::vector&lt;const caffe::LayerParameter*&gt; targetLayers;</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string&amp; requestedOutputName : <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a86cb41745deebd9b0ccf157d97d4d9ca">m_RequestedOutputs</a>)</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>&#160;    {</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>&#160; 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in graph {}&quot;</span>,</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>&#160;                            requestedOutputName,</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>&#160;                            <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>&#160;        }</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>&#160;        targetLayers.push_back(nodeIt-&gt;second);</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>&#160;    }</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span>&#160;</div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>&#160;    <span class="comment">// Sorts them into a linear ordering such that all inputs of a node are before the node itself.</span></div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>&#160;    std::vector&lt;const caffe::LayerParameter*&gt; sortedNodes;</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>&#160;    <span class="keywordflow">if</span> (!armnnUtils::GraphTopologicalSort&lt;const caffe::LayerParameter*&gt;(</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span>&#160;        targetLayers,</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>&#160;        [<span class="keyword">this</span>](<span class="keyword">const</span> caffe::LayerParameter* node)</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>&#160;        {</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>&#160;            <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2f8fbd66c1a39a06d61fcb6536387d64">GetInputs</a>(*node);</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>&#160;        },</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>&#160;        sortedNodes))</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>&#160;    {</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>&#160;            fmt::format(<span class="stringliteral">&quot;Cycle detected in graph. #nodes: {} {}&quot;</span>,</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>&#160;                        sortedNodes.size(),</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>&#160;    }</div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span>&#160;</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>&#160;    <span class="comment">// Parses each node in order, knowing that all inputs of a node will be processed before the node itself.</span></div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">const</span> caffe::LayerParameter* current : sortedNodes)</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>&#160;    {</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>&#160;        <span class="keyword">auto</span> it = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a6fb0cd80a09cf767309175fb138d203b">ms_CaffeLayerNameToParsingFunctions</a>.find(current-&gt;type());</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>&#160;        <span class="keywordflow">if</span> (it == <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a6fb0cd80a09cf767309175fb138d203b">ms_CaffeLayerNameToParsingFunctions</a>.end())</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>&#160;        {</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>&#160;                fmt::format(<span class="stringliteral">&quot;Unsupported layer type: &#39;{}&#39; for layer {} {}&quot;</span>,</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>&#160;                            current-&gt;type(),</div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>&#160;                            current-&gt;name(),</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>&#160;                            <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>&#160;        }</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>&#160;        <span class="keyword">auto</span> func = it-&gt;second;</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>&#160;        (this-&gt;*func)(*current);</div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>&#160;    }</div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>&#160;</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>&#160;    <span class="comment">// Adds ArmNN output layers connected to each requested output.</span></div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string&amp; requestedOutput : m_RequestedOutputs)</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>&#160;    {</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>&#160;        <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>&amp; outputSlot = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(requestedOutput);</div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span>&#160;</div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>&#160;        <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> outputId = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a>&lt;<a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a>&gt;(</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>&#160;            <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a62d6d6cba9ed0d3ad63fffb40aec86b5">m_NetworkOutputsBindingInfo</a>.size());</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>-&gt;AddOutputLayer(outputId, requestedOutput.c_str());</div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>&#160;        outputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span>&#160;</div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span>&#160;        <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0c98e07875a82c71c65bbb53eb347561">TrackOutputBinding</a>(outputLayer, outputId, outputLayer-&gt;GetInputSlot(0).GetConnection()-&gt;GetTensorInfo());</div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span>&#160;    }</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span>&#160;}</div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span>&#160;</div><div class="line"><a name="l02139"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac915fb2df2772be3179e97b1e8287a2d"> 2139</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac915fb2df2772be3179e97b1e8287a2d">ICaffeParser::CaffeParserImpl::CreateNetworkFromTextFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>&#160;    <span class="keyword">const</span> std::map&lt;std::string, armnn::TensorShape&gt;&amp; inputShapes,</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>&#160;    <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; requestedOutputs)</div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>&#160;{</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>&#160;    FILE* fd = fopen(graphFile, <span class="stringliteral">&quot;r&quot;</span>);</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>&#160;</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>&#160;    <span class="keywordflow">if</span> (fd == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>&#160;    {</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.xhtml">FileNotFoundException</a>(</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>&#160;            fmt::format(<span class="stringliteral">&quot;Failed to open graph file: {} {}&quot;</span>,</div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>&#160;                        graphFile,</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>&#160;    }</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>&#160;</div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>&#160;    <span class="comment">// Parses the file into a message.</span></div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span>&#160;    NetParameter netParam;</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>&#160;    <span class="keyword">auto</span>         input   = <span class="keyword">new</span> google::protobuf::io::FileInputStream(fileno(fd));</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>&#160;    <span class="keywordtype">bool</span>         success = google::protobuf::TextFormat::Parse(input, &amp;netParam);</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>&#160;    <span class="keyword">delete</span> input;</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>&#160;    fclose(fd);</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>&#160;</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>&#160;    <span class="keywordflow">if</span> (!success)</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>&#160;    {</div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>&#160;            fmt::format(<span class="stringliteral">&quot;Failed to parse graph file: {} {}&quot;</span>,</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>&#160;                        graphFile,</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>&#160;    }</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>&#160;</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab0bb4b8a290f1c8acd3c3a0d9a6e9783">CreateNetworkFromNetParameter</a>(netParam, inputShapes, requestedOutputs);</div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>&#160;}</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>&#160;</div><div class="line"><a name="l02171"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a7e393f41f2330006fdf00f2840c6dd28"> 2171</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a7e393f41f2330006fdf00f2840c6dd28">ICaffeParser::CaffeParserImpl::CreateNetworkFromString</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* protoText,</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>&#160;    <span class="keyword">const</span> std::map&lt;std::string, armnn::TensorShape&gt;&amp; inputShapes,</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>&#160;    <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; requestedOutputs)</div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>&#160;{</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>&#160;    <span class="comment">// Parses the string into a message.</span></div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>&#160;    NetParameter netParam;</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>&#160;    <span class="keywordtype">bool</span>         success = google::protobuf::TextFormat::ParseFromString(protoText, &amp;netParam);</div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>&#160;</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>&#160;    <span class="keywordflow">if</span> (!success)</div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>&#160;    {</div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>&#160;            fmt::format(<span class="stringliteral">&quot;Failed to parse graph string {}&quot;</span>,</div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>&#160;    }</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>&#160;</div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab0bb4b8a290f1c8acd3c3a0d9a6e9783">CreateNetworkFromNetParameter</a>(netParam, inputShapes, requestedOutputs);</div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>&#160;}</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>&#160;</div><div class="line"><a name="l02189"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_caffe_parser.xhtml#afb0edadd00c78430efbdc02844ef379a"> 2189</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_caffe_parser_1_1_caffe_parser.xhtml#afb0edadd00c78430efbdc02844ef379a">CaffeParser::CreateNetworkFromBinaryFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>&#160;    <span class="keyword">const</span> std::map&lt;std::string, armnn::TensorShape&gt;&amp; inputShapes,</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>&#160;    <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; requestedOutputs)</div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>&#160;{</div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>&#160;    FILE* fd = fopen(graphFile, <span class="stringliteral">&quot;rb&quot;</span>);</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>&#160;</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>&#160;    <span class="keywordflow">if</span> (fd == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>&#160;    {</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.xhtml">FileNotFoundException</a>(</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>&#160;            fmt::format(<span class="stringliteral">&quot;Failed to open graph file at: {} {}&quot;</span>,</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>&#160;                        graphFile,</div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span>&#160;    }</div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span>&#160;</div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>&#160;    <span class="comment">// Parses the file into a message.</span></div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span>&#160;    NetParameter netParam;</div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span>&#160;</div><div class="line"><a name="l02206"></a><span class="lineno"> 2206</span>&#160;    FileInputStream  inStream(fileno(fd));</div><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span>&#160;    CodedInputStream codedStream(&amp;inStream);</div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>&#160;    codedStream.SetTotalBytesLimit(INT_MAX);</div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span>&#160;    <span class="keywordtype">bool</span> success = netParam.ParseFromCodedStream(&amp;codedStream);</div><div class="line"><a name="l02210"></a><span class="lineno"> 2210</span>&#160;    fclose(fd);</div><div class="line"><a name="l02211"></a><span class="lineno"> 2211</span>&#160;</div><div class="line"><a name="l02212"></a><span class="lineno"> 2212</span>&#160;    <span class="keywordflow">if</span> (!success)</div><div class="line"><a name="l02213"></a><span class="lineno"> 2213</span>&#160;    {</div><div class="line"><a name="l02214"></a><span class="lineno"> 2214</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>&#160;            fmt::format(<span class="stringliteral">&quot;Failed to parse protobuf file: {} {}&quot;</span>,</div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span>&#160;                        graphFile,</div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span>&#160;    }</div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span>&#160;</div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab0bb4b8a290f1c8acd3c3a0d9a6e9783">CreateNetworkFromNetParameter</a>(netParam, inputShapes, requestedOutputs);</div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span>&#160;}</div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span>&#160;</div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span>&#160;<span class="comment">// Note: can move to CaffeParser when/if we optimise the text/string format</span></div><div class="line"><a name="l02224"></a><span class="lineno"> 2224</span>&#160;<span class="comment">//       to load on a layer by layer basis</span></div><div class="line"><a name="l02225"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab0bb4b8a290f1c8acd3c3a0d9a6e9783"> 2225</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab0bb4b8a290f1c8acd3c3a0d9a6e9783">ICaffeParser::CaffeParserImpl::CreateNetworkFromNetParameter</a>(NetParameter&amp; netParam,</div><div class="line"><a name="l02226"></a><span class="lineno"> 2226</span>&#160;    <span class="keyword">const</span> std::map&lt;std::string, armnn::TensorShape&gt;&amp; inputShapes,</div><div class="line"><a name="l02227"></a><span class="lineno"> 2227</span>&#160;    <span class="keyword">const</span> std::vector&lt;std::string&gt;&amp; requestedOutputs)</div><div class="line"><a name="l02228"></a><span class="lineno"> 2228</span>&#160;{</div><div class="line"><a name="l02229"></a><span class="lineno"> 2229</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac62e2558c14e01605f2b4e1e21cdd1e8">m_NetworkInputsBindingInfo</a>.clear();</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a62d6d6cba9ed0d3ad63fffb40aec86b5">m_NetworkOutputsBindingInfo</a>.clear();</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span>&#160;</div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a> = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span>&#160;</div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a76ea67f3f7d1d5835c5a92b65dc0854c">m_InputShapes</a> = inputShapes;</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>&#160;    <span class="keywordflow">if</span> (requestedOutputs.size() == 0)</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>&#160;    {</div><div class="line"><a name="l02237"></a><span class="lineno"> 2237</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">&quot;requestedOutputs must have at least one entry&quot;</span>);</div><div class="line"><a name="l02238"></a><span class="lineno"> 2238</span>&#160;    }</div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a86cb41745deebd9b0ccf157d97d4d9ca">m_RequestedOutputs</a> = requestedOutputs;</div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span>&#160;</div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span>&#160;    <span class="keywordflow">try</span></div><div class="line"><a name="l02242"></a><span class="lineno"> 2242</span>&#160;    {</div><div class="line"><a name="l02243"></a><span class="lineno"> 2243</span>&#160;        <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c5eed5d48d21a8b7e3bcd2cab519217">LoadNetParam</a>(netParam);</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>&#160;    }</div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>&#160;    <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>&amp; e)</div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>&#160;    {</div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span>&#160;        <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ae89a123aad1c66a76c398b7af216aae4">Cleanup</a>();</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span>&#160;        <span class="keywordflow">throw</span> e;</div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span>&#160;    }</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span>&#160;</div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ae89a123aad1c66a76c398b7af216aae4">Cleanup</a>();</div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span>&#160;</div><div class="line"><a name="l02253"></a><span class="lineno"> 2253</span>&#160;    <span class="keywordflow">return</span> move(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>);</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span>&#160;}</div><div class="line"><a name="l02255"></a><span class="lineno"> 2255</span>&#160;</div><div class="line"><a name="l02256"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa09a8bb02eed50715082d8b7fccd2f8d"> 2256</a></span>&#160;<span class="keyword">const</span> std::string <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa09a8bb02eed50715082d8b7fccd2f8d">ICaffeParser::CaffeParserImpl::GetVersion</a>()</div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span>&#160;{</div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="include_2armnn_caffe_parser_2_version_8hpp.xhtml#af3d53d50a9ddd493c4b40e44a82e2b44">CAFFE_PARSER_VERSION</a>;</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span>&#160;}</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span>&#160;</div><div class="line"><a name="l02261"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ae89a123aad1c66a76c398b7af216aae4"> 2261</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ae89a123aad1c66a76c398b7af216aae4">ICaffeParser::CaffeParserImpl::Cleanup</a>() {</div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>&#160;    <span class="comment">// cleanup, in case we reuse this parser</span></div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a76ea67f3f7d1d5835c5a92b65dc0854c">m_InputShapes</a>.clear();</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a86cb41745deebd9b0ccf157d97d4d9ca">m_RequestedOutputs</a>.clear();</div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>&#160;    <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">m_ArmnnOutputSlotForCaffeTop</a>.clear();</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span>&#160;    <span class="comment">// NOTE: when we get the text/string format</span></div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>&#160; 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<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::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#l00454">Descriptors.hpp:454</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_ac2dac3b61c94de52093616be4ab17f8d"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">armnn::IConnectableLayer::GetNumOutputSlots</a></div><div class="ttdeci">virtual unsigned int GetNumOutputSlots() const =0</div><div class="ttdoc">Returns the number of connectable output slots. </div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a3311e9dc3436fe83ef22c5f530fd3234"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a3311e9dc3436fe83ef22c5f530fd3234">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseSplitLayer</a></div><div class="ttdeci">void ParseSplitLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01881">CaffeParser.cpp:1881</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_adcb87456482d5df17ef09eca1a808091"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#adcb87456482d5df17ef09eca1a808091">armnnCaffeParser::ICaffeParser::CaffeParserImpl::AddConvLayerWithDepthwiseConv</a></div><div class="ttdeci">void AddConvLayerWithDepthwiseConv(const caffe::LayerParameter &amp;layerParam, const armnn::Convolution2dDescriptor &amp;desc, unsigned int kernelW, unsigned int kernelH)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00825">CaffeParser.cpp:825</a></div></div>
<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a></div><div class="ttdoc">A ViewsDescriptor for the SplitterLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00206">Descriptors.hpp:206</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00062">INetwork.hpp:62</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a77f532a6a82afeb6e79957726a9517a5"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77f532a6a82afeb6e79957726a9517a5">armnnCaffeParser::ICaffeParser::CaffeParserImpl::AddDeconvLayerWithSplits</a></div><div class="ttdeci">void AddDeconvLayerWithSplits(const caffe::LayerParameter &amp;layerParam, const armnn::TransposeConvolution2dDescriptor &amp;desc, unsigned int kernelW, unsigned int kernelH)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00637">CaffeParser.cpp:637</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Pooling2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00371">Descriptors.hpp:371</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a76ea67f3f7d1d5835c5a92b65dc0854c"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a76ea67f3f7d1d5835c5a92b65dc0854c">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_InputShapes</a></div><div class="ttdeci">std::map&lt; std::string, armnn::TensorShape &gt; m_InputShapes</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00144">CaffeParser.hpp:144</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8526ea7cf860d8e7f8340e9f9354f9f0"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">armnn::NormalizationDescriptor::m_K</a></div><div class="ttdeci">float m_K</div><div class="ttdoc">Kappa value used for the across channel normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00601">Descriptors.hpp:601</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a7785119cfebd2b02ba3be888965e52ba"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a7785119cfebd2b02ba3be888965e52ba">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseLRNLayer</a></div><div class="ttdeci">void ParseLRNLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01468">CaffeParser.cpp:1468</a></div></div>
<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml_a214c3636fdf0ea5bac8edb42d0e6c7f0"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml#a214c3636fdf0ea5bac8edb42d0e6c7f0">armnn::SoftmaxDescriptor::m_Axis</a></div><div class="ttdeci">int m_Axis</div><div class="ttdoc">Scalar, defaulted to the last index (-1), specifying the dimension the activation will be performed o...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00154">Descriptors.hpp:154</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a></div><div class="ttdoc">A TransposeConvolution2dDescriptor for the TransposeConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01213">Descriptors.hpp:1213</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00187">Tensor.hpp:187</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a4b1fdcb1985af12dd1848a9ffa5d3271"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a4b1fdcb1985af12dd1848a9ffa5d3271">armnnCaffeParser::ICaffeParser::CaffeParserImpl::GetNetworkOutputBindingInfo</a></div><div class="ttdeci">BindingPointInfo GetNetworkOutputBindingInfo(const std::string &amp;name) const</div><div class="ttdoc">Retrieves binding info (layer id and tensor info) for the network output identified by the given laye...</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00327">CaffeParser.cpp:327</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Pooling2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00365">Descriptors.hpp:365</a></div></div>
<div class="ttc" id="_utils_8hpp_xhtml"><div class="ttname"><a href="_utils_8hpp.xhtml">Utils.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a7e393f41f2330006fdf00f2840c6dd28"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a7e393f41f2330006fdf00f2840c6dd28">armnnCaffeParser::ICaffeParser::CaffeParserImpl::CreateNetworkFromString</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromString(const char *protoText, const std::map&lt; std::string, armnn::TensorShape &gt; &amp;inputShapes, const std::vector&lt; std::string &gt; &amp;requestedOutputs)</div><div class="ttdoc">Creates the network directly from protobuf text in a string. Useful for debugging/testing. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02171">CaffeParser.cpp:2171</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_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_ac915fb2df2772be3179e97b1e8287a2d"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac915fb2df2772be3179e97b1e8287a2d">armnnCaffeParser::ICaffeParser::CaffeParserImpl::CreateNetworkFromTextFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromTextFile(const char *graphFile, const std::map&lt; std::string, armnn::TensorShape &gt; &amp;inputShapes, const std::vector&lt; std::string &gt; &amp;requestedOutputs)</div><div class="ttdoc">Create the network from a protobuf text file on disk. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02139">CaffeParser.cpp:2139</a></div></div>
<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_a281fcaec86e17c97f7b8402633f6b55a"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">armnn::FullyConnectedDescriptor::m_TransposeWeightMatrix</a></div><div class="ttdeci">bool m_TransposeWeightMatrix</div><div class="ttdoc">Enable/disable transpose weight matrix. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00404">Descriptors.hpp:404</a></div></div>
<div class="ttc" id="namespacegoogle_1_1protobuf_1_1io_xhtml"><div class="ttname"><a href="namespacegoogle_1_1protobuf_1_1io.xhtml">io</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a95799625a4aae0ed73838cbfa3530c1b"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a95799625a4aae0ed73838cbfa3530c1b">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseScaleLayer</a></div><div class="ttdeci">void ParseScaleLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01831">CaffeParser.cpp:1831</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_afb7e4da478bab76261963479baad5788"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#afb7e4da478bab76261963479baad5788">armnnCaffeParser::ICaffeParser::CaffeParserImpl::GetBindingInfo</a></div><div class="ttdeci">static std::pair&lt; armnn::LayerBindingId, armnn::TensorInfo &gt; GetBindingInfo(const std::string &amp;layerName, const char *bindingPointDesc, const std::unordered_map&lt; std::string, BindingPointInfo &gt; &amp;bindingInfos)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00332">CaffeParser.cpp:332</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6d8fb685cc1ff224f25aa127fcf62c86"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">armnn::Pooling2dDescriptor::m_PoolWidth</a></div><div class="ttdeci">uint32_t m_PoolWidth</div><div class="ttdoc">Pooling width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00373">Descriptors.hpp:373</a></div></div>
<div class="ttc" id="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="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a174279be57d7596eeb04c6b7f7510f99"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">armnn::NormalizationDescriptor::m_Alpha</a></div><div class="ttdeci">float m_Alpha</div><div class="ttdoc">Alpha value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00597">Descriptors.hpp:597</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00490">Descriptors.hpp:490</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::TransposeConvolution2dDescriptor::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#l01254">Descriptors.hpp:1254</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_aa4c22681675806fa2c5fbf403d49c628"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa4c22681675806fa2c5fbf403d49c628">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseDropoutLayer</a></div><div class="ttdeci">void ParseDropoutLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01900">CaffeParser.cpp:1900</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_caffe_parser_xhtml_a82fc903eb5648250a6d82371a94772a3"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_caffe_parser.xhtml#a82fc903eb5648250a6d82371a94772a3">armnnCaffeParser::CaffeParser::CaffeParser</a></div><div class="ttdeci">CaffeParser()</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00316">CaffeParser.cpp:316</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a0c98e07875a82c71c65bbb53eb347561"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0c98e07875a82c71c65bbb53eb347561">armnnCaffeParser::ICaffeParser::CaffeParserImpl::TrackOutputBinding</a></div><div class="ttdeci">void TrackOutputBinding(armnn::IConnectableLayer *layer, armnn::LayerBindingId id, const armnn::TensorInfo &amp;tensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01923">CaffeParser.cpp:1923</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">armnn::BatchNormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Value to add to the variance. Used to avoid dividing by zero. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00639">Descriptors.hpp:639</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a8c29d6ea9b4186d69aad5961c910939c"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">armnn::Pooling2dDescriptor::m_PaddingMethod</a></div><div class="ttdeci">PaddingMethod m_PaddingMethod</div><div class="ttdoc">The padding method to be used. (Exclude, IgnoreValue). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00383">Descriptors.hpp:383</a></div></div>
<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml_ab1ae6f520bb1a4da191a0ae907477f23"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">armnn::ArgMinMaxDescriptor::m_Function</a></div><div class="ttdeci">ArgMinMaxFunction m_Function</div><div class="ttdoc">Specify if the function is to find Min or Max. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00070">Descriptors.hpp:70</a></div></div>
<div class="ttc" id="classarmnn_1_1_file_not_found_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_file_not_found_exception.xhtml">armnn::FileNotFoundException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00086">Exceptions.hpp:86</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a86cb41745deebd9b0ccf157d97d4d9ca"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a86cb41745deebd9b0ccf157d97d4d9ca">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_RequestedOutputs</a></div><div class="ttdeci">std::vector&lt; std::string &gt; m_RequestedOutputs</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00149">CaffeParser.hpp:149</a></div></div>
<div class="ttc" id="include_2armnn_caffe_parser_2_version_8hpp_xhtml_af3d53d50a9ddd493c4b40e44a82e2b44"><div class="ttname"><a href="include_2armnn_caffe_parser_2_version_8hpp.xhtml#af3d53d50a9ddd493c4b40e44a82e2b44">CAFFE_PARSER_VERSION</a></div><div class="ttdeci">#define CAFFE_PARSER_VERSION</div><div class="ttdoc">CAFFE_PARSER_VERSION: &quot;X.Y.Z&quot; where: X = Major version number Y = Minor version number Z = Patch vers...</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_caffe_parser_2_version_8hpp_source.xhtml#l00025">Version.hpp:25</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Pooling2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00369">Descriptors.hpp:369</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00440">Descriptors.hpp:440</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.dox:6</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::TransposeConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01248">Descriptors.hpp:1248</a></div></div>
<div class="ttc" id="namespacearmnn_caffe_parser_xhtml"><div class="ttname"><a href="namespacearmnn_caffe_parser.xhtml">armnnCaffeParser</a></div><div class="ttdoc">Caffe networks are loaded from protobuf files (binary or text) using the protobuf library and the gen...</div><div class="ttdef"><b>Definition:</b> <a href="_i_caffe_parser_8hpp_source.xhtml#l00016">ICaffeParser.hpp:16</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_aa09a8bb02eed50715082d8b7fccd2f8d"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa09a8bb02eed50715082d8b7fccd2f8d">armnnCaffeParser::ICaffeParser::CaffeParserImpl::GetVersion</a></div><div class="ttdeci">static const std::string GetVersion()</div><div class="ttdoc">Retrieve version in X.Y.Z form. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02256">CaffeParser.cpp:2256</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_aa0f0ff1cae05c1a0d7cc11b498714312"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa0f0ff1cae05c1a0d7cc11b498714312">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseConcatLayer</a></div><div class="ttdeci">void ParseConcatLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01708">CaffeParser.cpp:1708</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::Convolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation along y axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00452">Descriptors.hpp:452</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a9c5eed5d48d21a8b7e3bcd2cab519217"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c5eed5d48d21a8b7e3bcd2cab519217">armnnCaffeParser::ICaffeParser::CaffeParserImpl::LoadNetParam</a></div><div class="ttdeci">void LoadNetParam(caffe::NetParameter &amp;netParameter)</div><div class="ttdoc">does the actual conversion from caffe::NetParameter to armnn::INetwork </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02043">CaffeParser.cpp:2043</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a5f5e6255b21fdf458d3733bbdcdc4af5"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5f5e6255b21fdf458d3733bbdcdc4af5">armnnCaffeParser::ICaffeParser::CaffeParserImpl::TrackBindingPoint</a></div><div class="ttdeci">static void TrackBindingPoint(armnn::IConnectableLayer *layer, armnn::LayerBindingId id, const armnn::TensorInfo &amp;tensorInfo, const char *bindingPointDesc, std::unordered_map&lt; std::string, BindingPointInfo &gt; &amp;nameToBindingInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01930">CaffeParser.cpp:1930</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Pooling2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00377">Descriptors.hpp:377</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_ab0bb4b8a290f1c8acd3c3a0d9a6e9783"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab0bb4b8a290f1c8acd3c3a0d9a6e9783">armnnCaffeParser::ICaffeParser::CaffeParserImpl::CreateNetworkFromNetParameter</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromNetParameter(caffe::NetParameter &amp;netParam, const std::map&lt; std::string, armnn::TensorShape &gt; &amp;inputShapes, const std::vector&lt; std::string &gt; &amp;requestedOutputs)</div><div class="ttdoc">Parses a NetParameter loaded into memory from one of the other CreateNetwork*. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02225">CaffeParser.cpp:2225</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ab8cf8f9fb6792e654c2d8d8382f6f01b"><div class="ttname"><a href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a></div><div class="ttdeci">int LayerBindingId</div><div class="ttdoc">Type of identifiers for bindable layers (inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00210">Types.hpp:210</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &amp;tensorInfo)=0</div></div>
<div class="ttc" id="_numeric_cast_8hpp_xhtml"><div class="ttname"><a href="_numeric_cast_8hpp.xhtml">NumericCast.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a05945f080edf694b631960728b87aadb"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">armnn::NormalizationDescriptor::m_NormMethodType</a></div><div class="ttdeci">NormalizationAlgorithmMethod m_NormMethodType</div><div class="ttdoc">Normalization method algorithm to use (LocalBrightness, LocalContrast). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00593">Descriptors.hpp:593</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_xhtml_add49602ee9cd2bd16c1c4ccd25555d8e"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#add49602ee9cd2bd16c1c4ccd25555d8e">armnnCaffeParser::ICaffeParser::CreateNetworkFromTextFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromTextFile(const char *graphFile, const std::map&lt; std::string, armnn::TensorShape &gt; &amp;inputShapes, const std::vector&lt; std::string &gt; &amp;requestedOutputs)</div><div class="ttdoc">Create the network from a protobuf text file on the disk. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00085">CaffeParser.cpp:85</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &amp;newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00189">Tensor.hpp:189</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a5ce905b7412e68d588e08f4afc333aac"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5ce905b7412e68d588e08f4afc333aac">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseArgmaxLayer</a></div><div class="ttdeci">void ParseArgmaxLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01382">CaffeParser.cpp:1382</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a62d6d6cba9ed0d3ad63fffb40aec86b5"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a62d6d6cba9ed0d3ad63fffb40aec86b5">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_NetworkOutputsBindingInfo</a></div><div class="ttdeci">std::unordered_map&lt; std::string, BindingPointInfo &gt; m_NetworkOutputsBindingInfo</div><div class="ttdoc">maps output layer names to their corresponding ids and tensor infos </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00140">CaffeParser.hpp:140</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a9fea304829fe514d664de515ca5c3918"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9fea304829fe514d664de515ca5c3918">armnnCaffeParser::ICaffeParser::CaffeParserImpl::AddConvLayerWithSplits</a></div><div class="ttdeci">void AddConvLayerWithSplits(const caffe::LayerParameter &amp;layerParam, const armnn::Convolution2dDescriptor &amp;desc, unsigned int kernelW, unsigned int kernelH)</div><div class="ttdoc">ParseConv may use these helpers depending on the group parameter. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00447">CaffeParser.cpp:447</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_xhtml"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml">armnnCaffeParser::ICaffeParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_caffe_parser_8hpp_source.xhtml#l00024">ICaffeParser.hpp:24</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a5699e8606c37d18c03910b242cd1b010"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">armnn::Pooling2dDescriptor::m_PoolHeight</a></div><div class="ttdeci">uint32_t m_PoolHeight</div><div class="ttdoc">Pooling height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00375">Descriptors.hpp:375</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a8b053a6c449d0814cc831c916c126668"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a8b053a6c449d0814cc831c916c126668">armnnCaffeParser::ICaffeParser::CaffeParserImpl::GetNetworkInputBindingInfo</a></div><div class="ttdeci">BindingPointInfo GetNetworkInputBindingInfo(const std::string &amp;name) const</div><div class="ttdoc">Retrieves binding info (layer id and tensor info) for the network input identified by the given layer...</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00322">CaffeParser.cpp:322</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00442">Descriptors.hpp:442</a></div></div>
<div class="ttc" id="namespacestd_xhtml"><div class="ttname"><a href="namespacestd.xhtml">std</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00147">BackendId.hpp:147</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00446">Descriptors.hpp:446</a></div></div>
<div class="ttc" id="_verification_helpers_8hpp_xhtml"><div class="ttname"><a href="_verification_helpers_8hpp.xhtml">VerificationHelpers.hpp</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a9650b8810d4e6734b255ca25d495fe06"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">armnnCaffeParser::ICaffeParser::CaffeParserImpl::GetArmnnOutputSlotForCaffeTop</a></div><div class="ttdeci">armnn::IOutputSlot &amp; GetArmnnOutputSlotForCaffeTop(const std::string &amp;caffeTopName) const</div><div class="ttdoc">Retrieves the Armnn IOutputSlot representing the given Caffe top. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01952">CaffeParser.cpp:1952</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Pooling2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00367">Descriptors.hpp:367</a></div></div>
<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_aae0893695f5803a3517985c7cb1ccb2e"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">armnn::ViewsDescriptor::SetViewSize</a></div><div class="ttdeci">Status SetViewSize(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the size of the views. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00315">Descriptors.cpp:315</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::ArgMinMaxFunction::Max</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a></div><div class="ttdoc">An output connection slot for a layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00038">INetwork.hpp:38</a></div></div>
<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml">armnn::ArgMinMaxDescriptor</a></div><div class="ttdoc">An ArgMinMaxDescriptor for ArgMinMaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00056">Descriptors.hpp:56</a></div></div>
<div class="ttc" id="_caffe_parser_8hpp_xhtml"><div class="ttname"><a href="_caffe_parser_8hpp.xhtml">CaffeParser.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00163">Descriptors.hpp:163</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_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a6fb0cd80a09cf767309175fb138d203b"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a6fb0cd80a09cf767309175fb138d203b">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ms_CaffeLayerNameToParsingFunctions</a></div><div class="ttdeci">static const std::map&lt; std::string, OperationParsingFunction &gt; ms_CaffeLayerNameToParsingFunctions</div><div class="ttdoc">Maps Caffe layer names to parsing member functions. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00134">CaffeParser.hpp:134</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#l00402">Descriptors.hpp:402</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_ac62e2558c14e01605f2b4e1e21cdd1e8"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac62e2558c14e01605f2b4e1e21cdd1e8">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_NetworkInputsBindingInfo</a></div><div class="ttdeci">std::unordered_map&lt; std::string, BindingPointInfo &gt; m_NetworkInputsBindingInfo</div><div class="ttdoc">maps input layer names to their corresponding ids and tensor infos </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00137">CaffeParser.hpp:137</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="_record_by_record_caffe_parser_8hpp_xhtml"><div class="ttname"><a href="_record_by_record_caffe_parser_8hpp.xhtml">RecordByRecordCaffeParser.hpp</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_caffe_parser_xhtml_afb0edadd00c78430efbdc02844ef379a"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_caffe_parser.xhtml#afb0edadd00c78430efbdc02844ef379a">armnnCaffeParser::CaffeParser::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">virtual armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile, const std::map&lt; std::string, armnn::TensorShape &gt; &amp;inputShapes, const std::vector&lt; std::string &gt; &amp;requestedOutputs) override</div><div class="ttdoc">Create the network from a protobuf binary file on disk. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02189">CaffeParser.cpp:2189</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a5698be69cbd5dfe6c28fcd9867e8cbed"><div class="ttname"><a href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a></div><div class="ttdeci">#define ARMNN_ASSERT(COND)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00014">Assert.hpp:14</a></div></div>
<div class="ttc" id="_graph_topological_sort_8hpp_xhtml"><div class="ttname"><a href="_graph_topological_sort_8hpp.xhtml">GraphTopologicalSort.hpp</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a8449e66d395c0525561e3c67b100bafe"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a8449e66d395c0525561e3c67b100bafe">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseReluLayer</a></div><div class="ttdeci">void ParseReluLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01442">CaffeParser.cpp:1442</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a1c0594bf03dfbb44029465d3466127b3"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1c0594bf03dfbb44029465d3466127b3">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseSoftmaxLayer</a></div><div class="ttdeci">void ParseSoftmaxLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01641">CaffeParser.cpp:1641</a></div></div>
<div class="ttc" id="_caffe_parser_8cpp_xhtml_a69f4a692d0095f6b19b0cd99cd75e465"><div class="ttname"><a href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a></div><div class="ttdeci">#define GET_OPTIONAL_WITH_FALLBACK(PARAM, PARAM_TYPE, OPTIONAL_VALUE, FALLBACK_VALUE, VALUE_TYPE, DEFAULT_VALUE)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00257">CaffeParser.cpp:257</a></div></div>
<div class="ttc" id="namespacearmnn_caffe_parser_xhtml_a33c76910f1980ffaa41c22e0151cce2a"><div class="ttname"><a href="namespacearmnn_caffe_parser.xhtml#a33c76910f1980ffaa41c22e0151cce2a">armnnCaffeParser::ICaffeParserPtr</a></div><div class="ttdeci">std::unique_ptr&lt; ICaffeParser, void(*)(ICaffeParser *parser)&gt; ICaffeParserPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_caffe_parser_8hpp_source.xhtml#l00022">ICaffeParser.hpp:22</a></div></div>
<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00025">Descriptors.hpp:25</a></div></div>
<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
<div class="ttc" id="_exceptions_8hpp_xhtml_aa3be76aec4ce713822a5ea1ecbb7bc61"><div class="ttname"><a href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a></div><div class="ttdeci">#define CHECK_LOCATION()</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00197">Exceptions.hpp:197</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a940483591995bb812cfcd1595dba83c3"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a940483591995bb812cfcd1595dba83c3">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseBatchNormLayer</a></div><div class="ttdeci">void ParseBatchNormLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01767">CaffeParser.cpp:1767</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a34f6df4b84de1e269bcf02efeecc3892"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a34f6df4b84de1e269bcf02efeecc3892">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseInnerProductLayer</a></div><div class="ttdeci">void ParseInnerProductLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01567">CaffeParser.cpp:1567</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00448">Descriptors.hpp:448</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a89631aa06b5c628c46674c202b40dbc5"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a89631aa06b5c628c46674c202b40dbc5">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ResolveInPlaceLayers</a></div><div class="ttdeci">void ResolveInPlaceLayers(caffe::NetParameter &amp;netParameter)</div><div class="ttdoc">Modifies the Caffe network to replace &quot;in-place&quot; layers (whose top() and bottom() are both the same) ...</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01987">CaffeParser.cpp:1987</a></div></div>
<div class="ttc" id="namespacecaffe_xhtml"><div class="ttname"><a href="namespacecaffe.xhtml">caffe</a></div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00016">CaffeParser.hpp:16</a></div></div>
<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a0767b1c7ee9cbd014fd97c701a954caa"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">armnnCaffeParser::ICaffeParser::CaffeParserImpl::SetArmnnOutputSlotForCaffeTop</a></div><div class="ttdeci">void SetArmnnOutputSlotForCaffeTop(const std::string &amp;caffeTopName, armnn::IOutputSlot &amp;armnnOutputSlot)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01968">CaffeParser.cpp:1968</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_afe1f0f09d49ad2befc01f8789187b7dd"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">armnn::NormalizationDescriptor::m_NormChannelType</a></div><div class="ttdeci">NormalizationAlgorithmChannel m_NormChannelType</div><div class="ttdoc">Normalization channel algorithm to use (Across, Within). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00591">Descriptors.hpp:591</a></div></div>
<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_a017b2990003a014234f13e999dc7c689"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">armnn::ActivationDescriptor::m_A</a></div><div class="ttdeci">float m_A</div><div class="ttdoc">Alpha upper bound value used by the activation functions. (BoundedReLu, Linear, TanH, Elu). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00050">Descriptors.hpp:50</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::Convolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation along x axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00450">Descriptors.hpp:450</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml"><div class="ttname"><a href="_assert_8hpp.xhtml">Assert.hpp</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_ab3329e4bcd8e42cd314f84c8260b06ad"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab3329e4bcd8e42cd314f84c8260b06ad">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParsePoolingLayer</a></div><div class="ttdeci">void ParsePoolingLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01270">CaffeParser.cpp:1270</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::TransposeConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01242">Descriptors.hpp:1242</a></div></div>
<div class="ttc" id="structarmnn_1_1_empty_optional_xhtml"><div class="ttname"><a href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a></div><div class="ttdoc">EmptyOptional is used to initialize the Optional class in case we want to have default value for an O...</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00032">Optional.hpp:32</a></div></div>
<div class="ttc" id="classarmnn_1_1_parse_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_parse_exception.xhtml">armnn::ParseException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00092">Exceptions.hpp:92</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a280670a263dc4fd40491f6d0a2737f44"><div class="ttname"><a href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">armnn::BindingPointInfo</a></div><div class="ttdeci">std::pair&lt; armnn::LayerBindingId, armnn::TensorInfo &gt; BindingPointInfo</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00261">Tensor.hpp:261</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::TransposeConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01250">Descriptors.hpp:1250</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a0031997bf43bd2747656c31e4977793a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">armnn::Pooling2dDescriptor::m_PoolType</a></div><div class="ttdeci">PoolingAlgorithm m_PoolType</div><div class="ttdoc">The pooling algorithm to use (Max. Average, L2). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00363">Descriptors.hpp:363</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::TransposeConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01252">Descriptors.hpp:1252</a></div></div>
<div class="ttc" id="_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</a></div></div>
<div class="ttc" id="_exceptions_8hpp_xhtml"><div class="ttname"><a href="_exceptions_8hpp.xhtml">Exceptions.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_affb5b68b3eba3ed45a06c7cde7781962"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">armnn::Pooling2dDescriptor::m_OutputShapeRounding</a></div><div class="ttdeci">OutputShapeRounding m_OutputShapeRounding</div><div class="ttdoc">The rounding method for the output shape. (Floor, Ceiling). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00381">Descriptors.hpp:381</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot &amp; GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a4ddfa6cb51f928114a8151b8f455f115"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a4ddfa6cb51f928114a8151b8f455f115">armnnCaffeParser::ICaffeParser::CaffeParserImpl::CaffeParserImpl</a></div><div class="ttdeci">CaffeParserImpl()</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00310">CaffeParser.cpp:310</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a375ca3cff9f1b005d1412dc5f3cf5b6e"><div class="ttname"><a href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t&lt; std::is_unsigned&lt; Source &gt;::value &amp;&amp;std::is_unsigned&lt; Dest &gt;::value, Dest &gt; numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00035">NumericCast.hpp:35</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml_acee63cd08da47910fc166a1990988fa8"><div class="ttname"><a href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout, const armnn::DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00038">TensorUtils.cpp:38</a></div></div>
<div class="ttc" id="include_2armnn_caffe_parser_2_version_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_caffe_parser_2_version_8hpp.xhtml">Version.hpp</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a99a846a21b3a6ec97cc1d4344b91df36"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a99a846a21b3a6ec97cc1d4344b91df36">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseEltwiseLayer</a></div><div class="ttdeci">void ParseEltwiseLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01663">CaffeParser.cpp:1663</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::TransposeConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01244">Descriptors.hpp:1244</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</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_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot &amp; GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a2db3ae8d422f17d455e0ba0cb6291d2a"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_Network</a></div><div class="ttdeci">armnn::INetworkPtr m_Network</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00142">CaffeParser.hpp:142</a></div></div>
<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml_a214c3636fdf0ea5bac8edb42d0e6c7f0"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml#a214c3636fdf0ea5bac8edb42d0e6c7f0">armnn::ArgMinMaxDescriptor::m_Axis</a></div><div class="ttdeci">int m_Axis</div><div class="ttdoc">Axis to reduce across the input tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00072">Descriptors.hpp:72</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a9c99d40a72e6f0c6e4ad92d21e44edca"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_ArmnnOutputSlotForCaffeTop</a></div><div class="ttdeci">std::unordered_map&lt; std::string, armnn::IOutputSlot * &gt; m_ArmnnOutputSlotForCaffeTop</div><div class="ttdoc">As we add armnn layers we store the armnn IOutputSlot which corresponds to the Caffe tops...</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00147">CaffeParser.hpp:147</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_afcc1c3a20bd2860e0ddd21674389246f"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">armnn::IConnectableLayer::GetName</a></div><div class="ttdeci">virtual const char * GetName() const =0</div><div class="ttdoc">Returns the name of the layer. </div></div>
<div class="ttc" id="_caffe_parser_8cpp_xhtml_a7553d91772274ed9b103824bbf7f75a5"><div class="ttname"><a href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a></div><div class="ttdeci">#define GET_OPTIONAL_WITH_VECTOR_FALLBACK(PARAM, PARAM_TYPE, OPTIONAL_VALUE, FALLBACK_VECTOR, VALUE_TYPE, DEFAULT_VALUE)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00225">CaffeParser.cpp:225</a></div></div>
<div class="ttc" id="_test_utils_8cpp_xhtml_a0b295acb179f15eb3fb862b32008f782"><div class="ttname"><a href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a></div><div class="ttdeci">void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &amp;tensorInfo, unsigned int fromIndex, unsigned int toIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00012">TestUtils.cpp:12</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr&lt; INetwork, void(*)(INetwork *network)&gt; INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00173">INetwork.hpp:173</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &amp;destination)=0</div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a2a1112c66d08e3760ecccf39c7854a90"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2a1112c66d08e3760ecccf39c7854a90">armnnCaffeParser::ICaffeParser::CaffeParserImpl::TrackInputBinding</a></div><div class="ttdeci">void TrackInputBinding(armnn::IConnectableLayer *layer, armnn::LayerBindingId id, const armnn::TensorInfo &amp;tensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01916">CaffeParser.cpp:1916</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00329">Descriptors.hpp:329</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_ae89a123aad1c66a76c398b7af216aae4"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ae89a123aad1c66a76c398b7af216aae4">armnnCaffeParser::ICaffeParser::CaffeParserImpl::Cleanup</a></div><div class="ttdeci">void Cleanup()</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02261">CaffeParser.cpp:2261</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a></div><div class="ttdoc">A NormalizationDescriptor for the NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00567">Descriptors.hpp:567</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a77e6c08b48c99fafa560805270503856"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">armnnCaffeParser::ICaffeParser::CaffeParserImpl::BlobShapeToTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo BlobShapeToTensorInfo(const caffe::BlobShape &amp;blobShape) const</div><div class="ttdoc">Converts Caffe&amp;#39;s protobuf tensor shape format to ArmNN&amp;#39;s. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00349">CaffeParser.cpp:349</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a2f8fbd66c1a39a06d61fcb6536387d64"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2f8fbd66c1a39a06d61fcb6536387d64">armnnCaffeParser::ICaffeParser::CaffeParserImpl::GetInputs</a></div><div class="ttdeci">std::vector&lt; const caffe::LayerParameter * &gt; GetInputs(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdoc">Find the Caffe layers listed as inputs (bottoms) for a given layer. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00374">CaffeParser.cpp:374</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a5cddc80538d5de7d36192e0fd2d09c63"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5cddc80538d5de7d36192e0fd2d09c63">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseConvLayer</a></div><div class="ttdeci">void ParseConvLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00915">CaffeParser.cpp:915</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a3a2636dd8414f2bb62c5fa097bdc9791"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a3a2636dd8414f2bb62c5fa097bdc9791">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseInputLayer</a></div><div class="ttdeci">void ParseInputLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdoc">Adds an armnn layer to m_Network given a Caffe LayerParameter of the correct type and is responsible ...</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00397">CaffeParser.cpp:397</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00510">Network.cpp:510</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorInfo::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00191">Tensor.hpp:191</a></div></div>
<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a></div><div class="ttdoc">A SoftmaxDescriptor for the SoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00139">Descriptors.hpp:139</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::NormalizationDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Beta value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00599">Descriptors.hpp:599</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_aa70c05f1aad12fbd9d9ec43ea4557b03"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">armnn::NormalizationDescriptor::m_NormSize</a></div><div class="ttdeci">uint32_t m_NormSize</div><div class="ttdoc">Depth radius value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00595">Descriptors.hpp:595</a></div></div>
<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_a2b125117aa61f9baf3a9cb8658aa61a2"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">armnn::ViewsDescriptor::SetViewOriginCoord</a></div><div class="ttdeci">Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the view origin coordinates. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00310">Descriptors.cpp:310</a></div></div>
<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_af10fa7883e3579950f477bee92a64844"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">armnn::ActivationDescriptor::m_Function</a></div><div class="ttdeci">ActivationFunction m_Function</div><div class="ttdoc">The activation function to use (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu, LeakyReLu, Abs, Sqrt, Square, Elu). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00048">Descriptors.hpp:48</a></div></div>
<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml_abce784834696eb928c620f1fafe71a8d"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml#abce784834696eb928c620f1fafe71a8d">armnn::ArgMinMaxDescriptor::m_Output_Type</a></div><div class="ttdeci">armnn::DataType m_Output_Type</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00074">Descriptors.hpp:74</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Pooling2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00379">Descriptors.hpp:379</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_ac3f705f208b5ee9f540577524b2ad513"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac3f705f208b5ee9f540577524b2ad513">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseDeconvLayer</a></div><div class="ttdeci">void ParseDeconvLayer(const caffe::LayerParameter &amp;layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01094">CaffeParser.cpp:1094</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00460">Descriptors.hpp:460</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a></div><div class="ttdoc">A BatchNormalizationDescriptor for the BatchNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00626">Descriptors.hpp:626</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00438">Descriptors.hpp:438</a></div></div>
<div class="ttc" id="namespacearmnn_caffe_parser_xhtml_af3fde7630e7c9df35cae9ed2b435dbed"><div class="ttname"><a href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">armnnCaffeParser::TensorDescToBlobShape</a></div><div class="ttdeci">BlobShape TensorDescToBlobShape(const TensorInfo &amp;desc)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00360">CaffeParser.cpp:360</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_a2b125117aa61f9baf3a9cb8658aa61a2"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">armnn::OriginsDescriptor::SetViewOriginCoord</a></div><div class="ttdeci">Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the view origin coordinates. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00167">Descriptors.cpp:167</a></div></div>
<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a1424f1bfbfc81d317b51053bbb315ef1"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1424f1bfbfc81d317b51053bbb315ef1">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_CaffeLayersByTopName</a></div><div class="ttdeci">std::map&lt; std::string, const caffe::LayerParameter * &gt; m_CaffeLayersByTopName</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00155">CaffeParser.hpp:155</a></div></div>
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