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| <div class="title">OnnxParser.cpp</div> </div> |
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| <a href="_onnx_parser_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div> |
| <div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017,2022-2024 Arm Ltd and Contributors. All rights reserved.</span></div> |
| <div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div> |
| <div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div> |
| <div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#include "<a class="code" href="_onnx_parser_8hpp.html">OnnxParser.hpp</a>"</span></div> |
| <div class="line"><a name="l00006"></a><span class="lineno"> 6</span>  </div> |
| <div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="include_2armnn_onnx_parser_2_version_8hpp.html">armnnOnnxParser/Version.hpp</a>"</span></div> |
| <div class="line"><a name="l00008"></a><span class="lineno"> 8</span>  </div> |
| <div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_descriptors_8hpp.html">armnn/Descriptors.hpp</a>></span></div> |
| <div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_assert_8hpp.html">armnn/utility/Assert.hpp</a>></span></div> |
| <div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_numeric_cast_8hpp.html">armnn/utility/NumericCast.hpp</a>></span></div> |
| <div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_parser_helper_8hpp.html">ParserHelper.hpp</a>></span></div> |
| <div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_verification_helpers_8hpp.html">VerificationHelpers.hpp</a>></span></div> |
| <div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  </div> |
| <div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <fmt/format.h></span></div> |
| <div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  </div> |
| <div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <google/protobuf/text_format.h></span></div> |
| <div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <google/protobuf/io/zero_copy_stream_impl.h></span></div> |
| <div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  </div> |
| <div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <iostream></span></div> |
| <div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <numeric></span></div> |
| <div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#include <<a class="code" href="_permute_8hpp.html">armnnUtils/Permute.hpp</a>></span></div> |
| <div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  </div> |
| <div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.html">armnn</a>;</div> |
| <div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  </div> |
| <div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn_onnx_parser.html">armnnOnnxParser</a></div> |
| <div class="line"><a name="l00027"></a><span class="lineno"> 27</span> {</div> |
| <div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  </div> |
| <div class="line"><a name="l00029"></a><span class="lineno"> 29</span> IOnnxParser::IOnnxParser() : pOnnxParserImpl(new OnnxParserImpl()) {}</div> |
| <div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  </div> |
| <div class="line"><a name="l00031"></a><span class="lineno"> 31</span> IOnnxParser::~IOnnxParser() = <span class="keywordflow">default</span>;</div> |
| <div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  </div> |
| <div class="line"><a name="l00033"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#a1ae1d4dfe89d26b84d371439d6815bfb"> 33</a></span> <a class="code" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html">IOnnxParser</a>* IOnnxParser::CreateRaw()</div> |
| <div class="line"><a name="l00034"></a><span class="lineno"> 34</span> {</div> |
| <div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordflow">return</span> <span class="keyword">new</span> <a class="code" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html">IOnnxParser</a>();</div> |
| <div class="line"><a name="l00036"></a><span class="lineno"> 36</span> }</div> |
| <div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  </div> |
| <div class="line"><a name="l00038"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#af9b9254fb8a084f0db4f7deff0498b20"> 38</a></span> <a class="code" href="namespacearmnn_onnx_parser.html#ac7dfccab29feeb5f33f1ec0183c1e123">IOnnxParserPtr</a> IOnnxParser::Create()</div> |
| <div class="line"><a name="l00039"></a><span class="lineno"> 39</span> {</div> |
| <div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <a class="code" href="_deprecated_8hpp.html#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div> |
| <div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_onnx_parser.html#ac7dfccab29feeb5f33f1ec0183c1e123">IOnnxParserPtr</a>(<a class="code" href="classarmnn_deserializer_1_1_i_deserializer.html#a85f0c438b389992a68adeb6af59f362d">CreateRaw</a>(), &IOnnxParser::Destroy);</div> |
| <div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <a class="code" href="_deprecated_8hpp.html#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div> |
| <div class="line"><a name="l00043"></a><span class="lineno"> 43</span> }</div> |
| <div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  </div> |
| <div class="line"><a name="l00045"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#a793da4fa60bf13f128c20d8def32c291"> 45</a></span> <span class="keywordtype">void</span> IOnnxParser::Destroy(<a class="code" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html">IOnnxParser</a>* parser)</div> |
| <div class="line"><a name="l00046"></a><span class="lineno"> 46</span> {</div> |
| <div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">delete</span> parser;</div> |
| <div class="line"><a name="l00048"></a><span class="lineno"> 48</span> }</div> |
| <div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  </div> |
| <div class="line"><a name="l00050"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#a6bf5861864c8828e59df24a7868c5439"> 50</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> IOnnxParser::CreateNetworkFromBinaryFile(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div> |
| <div class="line"><a name="l00051"></a><span class="lineno"> 51</span> {</div> |
| <div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">return</span> pOnnxParserImpl-><a class="code" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#a6bf5861864c8828e59df24a7868c5439">CreateNetworkFromBinaryFile</a>(graphFile);</div> |
| <div class="line"><a name="l00053"></a><span class="lineno"> 53</span> }</div> |
| <div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  </div> |
| <div class="line"><a name="l00055"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#aaa88c7afbe8e8f777d05f99a2a540a99"> 55</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> IOnnxParser::CreateNetworkFromBinary(<span class="keyword">const</span> std::vector<uint8_t>& binaryContent)</div> |
| <div class="line"><a name="l00056"></a><span class="lineno"> 56</span> {</div> |
| <div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">return</span> pOnnxParserImpl->CreateNetworkFromBinary(binaryContent);</div> |
| <div class="line"><a name="l00058"></a><span class="lineno"> 58</span> }</div> |
| <div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  </div> |
| <div class="line"><a name="l00060"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#aef7bfc4211ad6f06bea245a5769a0aa7"> 60</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> IOnnxParser::CreateNetworkFromBinary(<span class="keyword">const</span> std::vector<uint8_t>& binaryContent,</div> |
| <div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes)</div> |
| <div class="line"><a name="l00062"></a><span class="lineno"> 62</span> {</div> |
| <div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">return</span> pOnnxParserImpl->CreateNetworkFromBinary(binaryContent, inputShapes);</div> |
| <div class="line"><a name="l00064"></a><span class="lineno"> 64</span> }</div> |
| <div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  </div> |
| <div class="line"><a name="l00066"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#ae6e0c06fbaab2070091357ca9ed52d0c"> 66</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> IOnnxParser::CreateNetworkFromTextFile(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div> |
| <div class="line"><a name="l00067"></a><span class="lineno"> 67</span> {</div> |
| <div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordflow">return</span> pOnnxParserImpl->CreateNetworkFromTextFile(graphFile);</div> |
| <div class="line"><a name="l00069"></a><span class="lineno"> 69</span> }</div> |
| <div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  </div> |
| <div class="line"><a name="l00071"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#a7a50b3c283b44956158e43db2e0111d0"> 71</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> IOnnxParser::CreateNetworkFromString(<span class="keyword">const</span> std::string& protoText)</div> |
| <div class="line"><a name="l00072"></a><span class="lineno"> 72</span> {</div> |
| <div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">return</span> pOnnxParserImpl->CreateNetworkFromString(protoText);</div> |
| <div class="line"><a name="l00074"></a><span class="lineno"> 74</span> }</div> |
| <div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  </div> |
| <div class="line"><a name="l00076"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#aed9ec6b80a244474caefd93f4c24df2f"> 76</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> IOnnxParser::CreateNetworkFromBinaryFile(</div> |
| <div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div> |
| <div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes)</div> |
| <div class="line"><a name="l00079"></a><span class="lineno"> 79</span> {</div> |
| <div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordflow">return</span> pOnnxParserImpl->CreateNetworkFromBinaryFile(graphFile, inputShapes);</div> |
| <div class="line"><a name="l00081"></a><span class="lineno"> 81</span> }</div> |
| <div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  </div> |
| <div class="line"><a name="l00083"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#af5ae13b67c1322833d2ced8af669ed6e"> 83</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> IOnnxParser::CreateNetworkFromTextFile(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div> |
| <div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes)</div> |
| <div class="line"><a name="l00085"></a><span class="lineno"> 85</span> {</div> |
| <div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keywordflow">return</span> pOnnxParserImpl->CreateNetworkFromTextFile(graphFile, inputShapes);</div> |
| <div class="line"><a name="l00087"></a><span class="lineno"> 87</span> }</div> |
| <div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  </div> |
| <div class="line"><a name="l00089"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#af195defcd30d5160ce5d14788fd6285a"> 89</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> IOnnxParser::CreateNetworkFromString(<span class="keyword">const</span> std::string& protoText,</div> |
| <div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes)</div> |
| <div class="line"><a name="l00091"></a><span class="lineno"> 91</span> {</div> |
| <div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">return</span> pOnnxParserImpl->CreateNetworkFromString(protoText, inputShapes);</div> |
| <div class="line"><a name="l00093"></a><span class="lineno"> 93</span> }</div> |
| <div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  </div> |
| <div class="line"><a name="l00095"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#a8b053a6c449d0814cc831c916c126668"> 95</a></span> <a class="code" href="namespacearmnn_onnx_parser.html#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> IOnnxParser::GetNetworkInputBindingInfo(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div> |
| <div class="line"><a name="l00096"></a><span class="lineno"> 96</span> <span class="keyword"></span>{</div> |
| <div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">return</span> pOnnxParserImpl->GetNetworkInputBindingInfo(name);</div> |
| <div class="line"><a name="l00098"></a><span class="lineno"> 98</span> }</div> |
| <div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  </div> |
| <div class="line"><a name="l00100"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#a4b1fdcb1985af12dd1848a9ffa5d3271"> 100</a></span> <a class="code" href="namespacearmnn_onnx_parser.html#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> IOnnxParser::GetNetworkOutputBindingInfo(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div> |
| <div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <span class="keyword"></span>{</div> |
| <div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordflow">return</span> pOnnxParserImpl->GetNetworkOutputBindingInfo(name);</div> |
| <div class="line"><a name="l00103"></a><span class="lineno"> 103</span> }</div> |
| <div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  </div> |
| <div class="line"><a name="l00105"></a><span class="lineno"> 105</span> <span class="keyword">namespace</span></div> |
| <div class="line"><a name="l00106"></a><span class="lineno"> 106</span> {</div> |
| <div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <span class="keywordtype">void</span> CheckValidDataType(std::initializer_list<onnx::TensorProto::DataType> validInputTypes,</div> |
| <div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a> actualValue,</div> |
| <div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* validExpr,</div> |
| <div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  std::string nodeName,</div> |
| <div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  std::string tensorName,</div> |
| <div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.html">armnn::CheckLocation</a>& location)</div> |
| <div class="line"><a name="l00113"></a><span class="lineno"> 113</span> {</div> |
| <div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keywordtype">bool</span> isValid = std::any_of(validInputTypes.begin(),</div> |
| <div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  validInputTypes.end(),</div> |
| <div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  [&actualValue](<a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a> x) { return x == actualValue; } );</div> |
| <div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keywordflow">if</span> (!isValid)</div> |
| <div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  {</div> |
| <div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  fmt::format(<span class="stringliteral">"Datatype {} is not valid for tensor '{}' of node '{}', not in {{{}}}. {}"</span>,</div> |
| <div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  onnx::TensorProto::DataType_Name(actualValue),</div> |
| <div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  tensorName,</div> |
| <div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  nodeName,</div> |
| <div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  validExpr,</div> |
| <div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  location.<a class="code" href="structarmnn_1_1_check_location.html#a5e3562cda960da001597e7dd5679b140">AsString</a>()));</div> |
| <div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  }</div> |
| <div class="line"><a name="l00127"></a><span class="lineno"> 127</span> }</div> |
| <div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  </div> |
| <div class="line"><a name="l00129"></a><span class="lineno"><a class="line" href="_onnx_parser_8cpp.html#a71cae957feb9162183d6f62fd549ffe1"> 129</a></span> <span class="preprocessor">#define CHECK_VALID_DATATYPE(NODE, TENSOR, ACTUAL, ...) \</span></div> |
| <div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="preprocessor">CheckValidDataType({__VA_ARGS__}, ACTUAL, #__VA_ARGS__, NODE, TENSOR, CHECK_LOCATION())</span></div> |
| <div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  </div> |
| <div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="keyword">using</span> StrTypeListPair = std::pair<const char*, std::initializer_list<onnx::TensorProto::DataType>>;</div> |
| <div class="line"><a name="l00133"></a><span class="lineno"><a class="line" href="_onnx_parser_8cpp.html#a5426a7adb280d1739cc6d66fe9ac1b9c"> 133</a></span> <span class="preprocessor">#define STR_LIST(...) StrTypeListPair(#__VA_ARGS__, {__VA_ARGS__})</span></div> |
| <div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  </div> |
| <div class="line"><a name="l00135"></a><span class="lineno"> 135</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Callable></div> |
| <div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="keywordtype">void</span> ReadMandatoryNodeAttributeImpl(<span class="keyword">const</span> onnx::NodeProto& node,</div> |
| <div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keyword">const</span> std::string& attribName,</div> |
| <div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  onnx::AttributeProto::AttributeType expectedType,</div> |
| <div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  Callable callable)</div> |
| <div class="line"><a name="l00140"></a><span class="lineno"> 140</span> {</div> |
| <div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keyword">auto</span> attribs = node.attribute();</div> |
| <div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordtype">int</span> attriNum = 0;</div> |
| <div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordflow">while</span> (attriNum < node.attribute_size())</div> |
| <div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  {</div> |
| <div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordflow">if</span> (attribs.Get(attriNum).name() == attribName)</div> |
| <div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  {</div> |
| <div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">if</span> (attribs.Get(attriNum).type() == expectedType)</div> |
| <div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  {</div> |
| <div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  callable(attribs.Get(attriNum));</div> |
| <div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  }</div> |
| <div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  {</div> |
| <div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Attribute {} of node {} expected to have {} as "</span></div> |
| <div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="stringliteral">"onnx::AttributeProto::AttributeType, but found {} instead {}"</span>,</div> |
| <div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  attribName,</div> |
| <div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  node.name(),</div> |
| <div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  onnx::AttributeProto::AttributeType_Name(expectedType),</div> |
| <div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  onnx::AttributeProto::AttributeType_Name(attribs.Get(attriNum).type()),</div> |
| <div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  }</div> |
| <div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordflow">break</span>;</div> |
| <div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  }</div> |
| <div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  ++attriNum;</div> |
| <div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  }</div> |
| <div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordflow">if</span> (attriNum == node.attribute_size())</div> |
| <div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  {</div> |
| <div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Could not find required attribute {} in node {} {}"</span>,</div> |
| <div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  attribName, node.name(), <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  }</div> |
| <div class="line"><a name="l00170"></a><span class="lineno"> 170</span> }</div> |
| <div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  </div> |
| <div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Callable></div> |
| <div class="line"><a name="l00173"></a><span class="lineno"> 173</span> <span class="keywordtype">void</span> ReadOptionalNodeAttributeImpl(<span class="keyword">const</span> onnx::NodeProto& node,</div> |
| <div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keyword">const</span> std::string& attribName,</div> |
| <div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  onnx::AttributeProto::AttributeType expectedType,</div> |
| <div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  Callable callable)</div> |
| <div class="line"><a name="l00177"></a><span class="lineno"> 177</span> {</div> |
| <div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keyword">auto</span> attribs = node.attribute();</div> |
| <div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> attriNum = 0; attriNum < node.attribute_size(); ++attriNum)</div> |
| <div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  {</div> |
| <div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordflow">if</span> (attribs.Get(attriNum).name() == attribName)</div> |
| <div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  {</div> |
| <div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">if</span> (attribs.Get(attriNum).type() == expectedType)</div> |
| <div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  {</div> |
| <div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  callable(attribs.Get(attriNum));</div> |
| <div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  }</div> |
| <div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  {</div> |
| <div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  fmt::format(<span class="stringliteral">"Attribute {} of node {} expected to have {} as onnx::AttributeProto::AttributeType, "</span></div> |
| <div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="stringliteral">"but found {} instead {}"</span>,</div> |
| <div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  attribName,</div> |
| <div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  node.name(),</div> |
| <div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  onnx::AttributeProto::AttributeType_Name(expectedType),</div> |
| <div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  onnx::AttributeProto::AttributeType_Name(attribs.Get(attriNum).type()),</div> |
| <div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  }</div> |
| <div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  }</div> |
| <div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  }</div> |
| <div class="line"><a name="l00200"></a><span class="lineno"> 200</span> }</div> |
| <div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  </div> |
| <div class="line"><a name="l00202"></a><span class="lineno"> 202</span> <span class="keywordtype">int</span> ReadMandatoryNodeIntAttribute(<span class="keyword">const</span> onnx::NodeProto& node,</div> |
| <div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">const</span> std::string& name)</div> |
| <div class="line"><a name="l00204"></a><span class="lineno"> 204</span> {</div> |
| <div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keywordtype">int</span> attribValue = 0;</div> |
| <div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  ReadMandatoryNodeAttributeImpl(node, name, onnx::AttributeProto::INT,</div> |
| <div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  [&attribValue](<span class="keyword">const</span> onnx::AttributeProto& attrValue)</div> |
| <div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  {</div> |
| <div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  attribValue = <a class="code" href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(attrValue.i());</div> |
| <div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  });</div> |
| <div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordflow">return</span> attribValue;</div> |
| <div class="line"><a name="l00212"></a><span class="lineno"> 212</span> }</div> |
| <div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  </div> |
| <div class="line"><a name="l00214"></a><span class="lineno"> 214</span> int64_t ReadOptionalNodeInt64Attribute(<span class="keyword">const</span> onnx::NodeProto& node,</div> |
| <div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keyword">const</span> std::string& name,</div> |
| <div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">const</span> int64_t defaultValue = 0)</div> |
| <div class="line"><a name="l00217"></a><span class="lineno"> 217</span> {</div> |
| <div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  int64_t attribValue = defaultValue;</div> |
| <div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INT,</div> |
| <div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  [&attribValue](<span class="keyword">const</span> onnx::AttributeProto& attrValue)</div> |
| <div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  {</div> |
| <div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  attribValue = attrValue.i();</div> |
| <div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  });</div> |
| <div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keywordflow">return</span> attribValue;</div> |
| <div class="line"><a name="l00225"></a><span class="lineno"> 225</span> }</div> |
| <div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  </div> |
| <div class="line"><a name="l00227"></a><span class="lineno"> 227</span> std::vector<uint32_t> ReadMandatoryNodeUint32ListAttribute(<span class="keyword">const</span> onnx::NodeProto& node,</div> |
| <div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keyword">const</span> std::string& name)</div> |
| <div class="line"><a name="l00229"></a><span class="lineno"> 229</span> {</div> |
| <div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  std::vector<uint32_t> attriList;</div> |
| <div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  ReadMandatoryNodeAttributeImpl(node, name, onnx::AttributeProto::INTS,</div> |
| <div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  [&attriList](<span class="keyword">const</span> onnx::AttributeProto& attrValue)</div> |
| <div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  {</div> |
| <div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> attriNum = 0; attriNum < attrValue.ints_size(); ++attriNum)</div> |
| <div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  {</div> |
| <div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  attriList.push_back(<a class="code" href="_verification_helpers_8hpp.html#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(attrValue.ints().Get(attriNum))));</div> |
| <div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  }</div> |
| <div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  });</div> |
| <div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keywordflow">return</span> attriList;</div> |
| <div class="line"><a name="l00240"></a><span class="lineno"> 240</span> }</div> |
| <div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  </div> |
| <div class="line"><a name="l00242"></a><span class="lineno"> 242</span> uint32_t ReadOptionalNodeUint32Attribute(<span class="keyword">const</span> onnx::NodeProto& node,</div> |
| <div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keyword">const</span> std::string& name,</div> |
| <div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keyword">const</span> uint32_t defaultVal = 0u)</div> |
| <div class="line"><a name="l00245"></a><span class="lineno"> 245</span> {</div> |
| <div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  uint32_t attribValue = defaultVal;</div> |
| <div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INT,</div> |
| <div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  [&attribValue](<span class="keyword">const</span> onnx::AttributeProto& attrValue)</div> |
| <div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  {</div> |
| <div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  attribValue = <a class="code" href="_verification_helpers_8hpp.html#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>((attrValue.i())));</div> |
| <div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  });</div> |
| <div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keywordflow">return</span> attribValue;</div> |
| <div class="line"><a name="l00253"></a><span class="lineno"> 253</span> }</div> |
| <div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  </div> |
| <div class="line"><a name="l00255"></a><span class="lineno"> 255</span> std::vector<uint32_t> ReadOptionalNodeUint32ListAttribute(<span class="keyword">const</span> onnx::NodeProto& node,</div> |
| <div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keyword">const</span> std::string& name)</div> |
| <div class="line"><a name="l00257"></a><span class="lineno"> 257</span> {</div> |
| <div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  std::vector<uint32_t> attriList;</div> |
| <div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INTS,</div> |
| <div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  [&attriList](<span class="keyword">const</span> onnx::AttributeProto& attrValue)</div> |
| <div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  {</div> |
| <div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> attriNum = 0; attriNum < attrValue.ints_size(); ++attriNum)</div> |
| <div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  {</div> |
| <div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  attriList.push_back(<a class="code" href="_verification_helpers_8hpp.html#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(attrValue.ints().Get(attriNum))));</div> |
| <div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  }</div> |
| <div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  });</div> |
| <div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  </div> |
| <div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keywordflow">return</span> attriList;</div> |
| <div class="line"><a name="l00269"></a><span class="lineno"> 269</span> }</div> |
| <div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  </div> |
| <div class="line"><a name="l00271"></a><span class="lineno"> 271</span> <span class="keywordtype">float</span> ReadOptionalNodeFloatAttribute(<span class="keyword">const</span> onnx::NodeProto& node,</div> |
| <div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keyword">const</span> std::string& name,</div> |
| <div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> defaultValue = 0.0f)</div> |
| <div class="line"><a name="l00274"></a><span class="lineno"> 274</span> {</div> |
| <div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordtype">float</span> attribValue = defaultValue;</div> |
| <div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::FLOAT,</div> |
| <div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  [&attribValue](<span class="keyword">const</span> onnx::AttributeProto& attrValue)</div> |
| <div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  {</div> |
| <div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  attribValue = attrValue.f();</div> |
| <div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  });</div> |
| <div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="keywordflow">return</span> attribValue;</div> |
| <div class="line"><a name="l00282"></a><span class="lineno"> 282</span> }</div> |
| <div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  </div> |
| <div class="line"><a name="l00284"></a><span class="lineno"> 284</span> std::string ReadOptionalNodeStringAttribute(<span class="keyword">const</span> onnx::NodeProto& node, <span class="keyword">const</span> std::string& name)</div> |
| <div class="line"><a name="l00285"></a><span class="lineno"> 285</span> {</div> |
| <div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  std::string attribValue = <span class="stringliteral">""</span>;</div> |
| <div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::STRING,</div> |
| <div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  [&attribValue](<span class="keyword">const</span> onnx::AttributeProto& attrValue)</div> |
| <div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  {</div> |
| <div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  attribValue = attrValue.s();</div> |
| <div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  });</div> |
| <div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordflow">return</span> attribValue;</div> |
| <div class="line"><a name="l00293"></a><span class="lineno"> 293</span> }</div> |
| <div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  </div> |
| <div class="line"><a name="l00295"></a><span class="lineno"> 295</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> <a class="code" href="namespacearmnn_deserializer.html#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(<span class="keyword">const</span> std::string& name, std::vector<unsigned int>& shape, <span class="keywordtype">int</span> data_type)</div> |
| <div class="line"><a name="l00296"></a><span class="lineno"> 296</span> {</div> |
| <div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> type;</div> |
| <div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keywordflow">switch</span>(data_type)</div> |
| <div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  {</div> |
| <div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keywordflow">case</span> onnx::TensorProto::FLOAT:</div> |
| <div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  {</div> |
| <div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  type = DataType::Float32;</div> |
| <div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keywordflow">break</span>;</div> |
| <div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  }</div> |
| <div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keywordflow">case</span> onnx::TensorProto::INT32:</div> |
| <div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordflow">case</span> onnx::TensorProto::INT64:</div> |
| <div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  {</div> |
| <div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  type = DataType::Signed32;</div> |
| <div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keywordflow">break</span>;</div> |
| <div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  }</div> |
| <div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keywordflow">default</span>:</div> |
| <div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  {</div> |
| <div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  fmt::format(<span class="stringliteral">"'{}' is not a currently supported datatype for tensor {}."</span></div> |
| <div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="stringliteral">" Supported dataTypes are FLOAT, INT32 and INT64. {}"</span>,</div> |
| <div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  onnx::TensorProto::DataType_Name(<span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a><span class="keyword">></span>(data_type)),</div> |
| <div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  name,</div> |
| <div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString() ));</div> |
| <div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  }</div> |
| <div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  }</div> |
| <div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  </div> |
| <div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="comment">// Scalar Tensor</span></div> |
| <div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="keywordflow">if</span> (shape.empty())</div> |
| <div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  {</div> |
| <div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>(Dimensionality::Scalar), type);</div> |
| <div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  }</div> |
| <div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  </div> |
| <div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="comment">// Dynamic Tensor</span></div> |
| <div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="keywordflow">if</span>(std::find(shape.begin(), shape.end(), 0) != shape.end())</div> |
| <div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  {</div> |
| <div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>(Dimensionality::NotSpecified), type);</div> |
| <div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  }</div> |
| <div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  </div> |
| <div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>(<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(shape.size()), shape.data()), type);</div> |
| <div class="line"><a name="l00335"></a><span class="lineno"> 335</span> }</div> |
| <div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  </div> |
| <div class="line"><a name="l00337"></a><span class="lineno"> 337</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> <a class="code" href="namespacearmnn_deserializer.html#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(<span class="keyword">const</span> onnx::ValueInfoProto& info)</div> |
| <div class="line"><a name="l00338"></a><span class="lineno"> 338</span> {</div> |
| <div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keyword">const</span> onnx::TensorShapeProto onnxShape = <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.type().tensor_type().shape();</div> |
| <div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  std::vector<unsigned int> shapeDims;</div> |
| <div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < onnxShape.dim_size(); ++i)</div> |
| <div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  {</div> |
| <div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  shapeDims.push_back(<a class="code" href="_verification_helpers_8hpp.html#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(onnxShape.dim(i).dim_value())));</div> |
| <div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  }</div> |
| <div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  </div> |
| <div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_deserializer.html#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(<a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.name(), shapeDims, <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.type().tensor_type().elem_type());</div> |
| <div class="line"><a name="l00347"></a><span class="lineno"> 347</span> }</div> |
| <div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  </div> |
| <div class="line"><a name="l00349"></a><span class="lineno"> 349</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> <a class="code" href="namespacearmnn_deserializer.html#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(<span class="keyword">const</span> onnx::TensorProto& tensor)</div> |
| <div class="line"><a name="l00350"></a><span class="lineno"> 350</span> {</div> |
| <div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  std::vector<unsigned int> shapeDims;</div> |
| <div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  </div> |
| <div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> dim: tensor.dims())</div> |
| <div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  {</div> |
| <div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  shapeDims.push_back(<a class="code" href="_verification_helpers_8hpp.html#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(dim)));</div> |
| <div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  }</div> |
| <div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  </div> |
| <div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_deserializer.html#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(tensor.name(), shapeDims, tensor.data_type());</div> |
| <div class="line"><a name="l00359"></a><span class="lineno"> 359</span> }</div> |
| <div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  </div> |
| <div class="line"><a name="l00361"></a><span class="lineno"> 361</span> std::string TensorInfoAsString(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>& info,</div> |
| <div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keyword">const</span> std::string& name,</div> |
| <div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a>& type)</div> |
| <div class="line"><a name="l00364"></a><span class="lineno"> 364</span> {</div> |
| <div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> shape = <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.GetShape();</div> |
| <div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  std::stringstream ss;</div> |
| <div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  ss << <span class="stringliteral">"tensor '"</span> << name << <span class="stringliteral">"' contains "</span></div> |
| <div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  << onnx::TensorProto::DataType_Name(type)</div> |
| <div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  << <span class="stringliteral">" and has shape ["</span>;</div> |
| <div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  </div> |
| <div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < shape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - 1; ++i)</div> |
| <div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  {</div> |
| <div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  ss << shape[i] << <span class="stringliteral">", "</span>;</div> |
| <div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  }</div> |
| <div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  ss << shape[shape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - 1] << <span class="stringliteral">"]"</span>;</div> |
| <div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="keywordflow">return</span> ss.str();</div> |
| <div class="line"><a name="l00377"></a><span class="lineno"> 377</span> }</div> |
| <div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  </div> |
| <div class="line"><a name="l00379"></a><span class="lineno"> 379</span> <span class="keywordtype">void</span> CalcPadding(uint32_t inputSize,</div> |
| <div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  uint32_t filterSize,</div> |
| <div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  uint32_t stride,</div> |
| <div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  uint32_t dilation,</div> |
| <div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  uint32_t* paddingFront,</div> |
| <div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  uint32_t* paddingBack,</div> |
| <div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordtype">bool</span> isUpper)</div> |
| <div class="line"><a name="l00386"></a><span class="lineno"> 386</span> {</div> |
| <div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  uint32_t outputSize = (inputSize + stride - 1) / stride;</div> |
| <div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  uint32_t dilatedSize = filterSize + (dilation - 1) * (filterSize - 1);</div> |
| <div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  uint32_t temp = (outputSize - 1) * stride + dilatedSize;</div> |
| <div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  *paddingFront = (temp - inputSize) / 2;</div> |
| <div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  *paddingBack = *paddingFront;</div> |
| <div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordflow">if</span>((temp - inputSize) % 2 == 1)</div> |
| <div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  {</div> |
| <div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keywordflow">if</span> (isUpper)</div> |
| <div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  {</div> |
| <div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  *paddingBack += 1;</div> |
| <div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  }</div> |
| <div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  {</div> |
| <div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  *paddingFront += 1;</div> |
| <div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  }</div> |
| <div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  }</div> |
| <div class="line"><a name="l00403"></a><span class="lineno"> 403</span> }</div> |
| <div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  </div> |
| <div class="line"><a name="l00405"></a><span class="lineno"> 405</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> ComputeReshapeInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>& targetShapeTensor,</div> |
| <div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>& inShape,</div> |
| <div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keyword">const</span> std::string& outName,</div> |
| <div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType = DataType::Float32)</div> |
| <div class="line"><a name="l00409"></a><span class="lineno"> 409</span> {</div> |
| <div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  std::vector<int> targetDims;</div> |
| <div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <span class="keywordflow">for</span>(uint i = 0; i < targetShapeTensor.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div> |
| <div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  {</div> |
| <div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="keywordtype">int</span> val = <a class="code" href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(targetShapeTensor[i]);</div> |
| <div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keywordflow">if</span>(val == 0)</div> |
| <div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  {</div> |
| <div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  targetDims.push_back(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(inShape[<span class="keyword">static_cast<</span>uint<span class="keyword">></span>(i)]));</div> |
| <div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  }</div> |
| <div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  {</div> |
| <div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  targetDims.push_back(val);</div> |
| <div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  }</div> |
| <div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  }</div> |
| <div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  </div> |
| <div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  std::vector<unsigned int> outDims(targetDims.begin(), targetDims.end());</div> |
| <div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keyword">const</span> <span class="keyword">auto</span> stretchDim = std::find(targetDims.begin(), targetDims.end(), -1);</div> |
| <div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keywordflow">if</span> (stretchDim != targetDims.end())</div> |
| <div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  {</div> |
| <div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keywordflow">if</span> (std::find(std::next(stretchDim), targetDims.end(), -1) != targetDims.end())</div> |
| <div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  {</div> |
| <div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  std::stringstream ss;</div> |
| <div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  ss << <span class="stringliteral">"[ "</span>;</div> |
| <div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keywordflow">for</span>(uint i = 0; i < targetDims.size() - 1; ++i)</div> |
| <div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  {</div> |
| <div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  ss << targetDims[i] << <span class="stringliteral">", "</span>;</div> |
| <div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  }</div> |
| <div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  ss << targetDims[targetDims.size() - 1] << <span class="stringliteral">" ]"</span>;</div> |
| <div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  </div> |
| <div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  fmt::format(<span class="stringliteral">"Error during creation of reshaped tensor '{}'. At most one component of shape can be "</span></div> |
| <div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="stringliteral">" -1 and here, shape is {} {}"</span>,</div> |
| <div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  outName,</div> |
| <div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  ss.str(),</div> |
| <div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  }</div> |
| <div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  </div> |
| <div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="keyword">auto</span> targetNumElements = armnn::numeric_cast<unsigned int>(</div> |
| <div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  std::accumulate(targetDims.begin(), targetDims.end(), -1, std::multiplies<int32_t>()));</div> |
| <div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="keyword">auto</span> stretchIndex = <span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(std::distance(targetDims.begin(), stretchDim));</div> |
| <div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keywordflow">if</span> (targetNumElements == 0)</div> |
| <div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  {</div> |
| <div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="keywordflow">if</span> (inShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() == 0)</div> |
| <div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  {</div> |
| <div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  outDims[stretchIndex] = 0;</div> |
| <div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  }</div> |
| <div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  {</div> |
| <div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  fmt::format(<span class="stringliteral">"Input to reshape is a tensor with elements, but the requested shape has 0. {}"</span>,</div> |
| <div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  }</div> |
| <div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  }</div> |
| <div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  {</div> |
| <div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  outDims[stretchIndex] = inShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / targetNumElements;</div> |
| <div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  }</div> |
| <div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  }</div> |
| <div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> outShape = <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>{<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outDims.size()), outDims.data()};</div> |
| <div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>(outShape, dataType);</div> |
| <div class="line"><a name="l00469"></a><span class="lineno"> 469</span> }</div> |
| <div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  </div> |
| <div class="line"><a name="l00471"></a><span class="lineno"> 471</span> } <span class="comment">//namespace</span></div> |
| <div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  </div> |
| <div class="line"><a name="l00473"></a><span class="lineno"> 473</span> <span class="keyword">const</span> std::map<std::string, OnnxParserImpl::OperationParsingFunction> OnnxParserImpl::m_ParserFunctions = {</div> |
| <div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  { <span class="stringliteral">"BatchNormalization"</span>, &OnnxParserImpl::ParseBatchNormalization},</div> |
| <div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  { <span class="stringliteral">"GlobalAveragePool"</span>, &OnnxParserImpl::ParseGlobalAveragePool},</div> |
| <div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  { <span class="stringliteral">"AveragePool"</span>, &OnnxParserImpl::ParseAveragePool },</div> |
| <div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  { <span class="stringliteral">"Clip"</span>, &OnnxParserImpl::ParseClip },</div> |
| <div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  { <span class="stringliteral">"Constant"</span>, &OnnxParserImpl::ParseConstant },</div> |
| <div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  { <span class="stringliteral">"MaxPool"</span>, &OnnxParserImpl::ParseMaxPool },</div> |
| <div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  { <span class="stringliteral">"Reshape"</span>, &OnnxParserImpl::ParseReshape },</div> |
| <div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  { <span class="stringliteral">"Sigmoid"</span>, &OnnxParserImpl::ParseSigmoid },</div> |
| <div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  { <span class="stringliteral">"Tanh"</span>, &OnnxParserImpl::ParseTanh },</div> |
| <div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  { <span class="stringliteral">"Relu"</span>, &OnnxParserImpl::ParseRelu },</div> |
| <div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  { <span class="stringliteral">"LeakyRelu"</span>, &OnnxParserImpl::ParseLeakyRelu },</div> |
| <div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  { <span class="stringliteral">"Conv"</span>, &OnnxParserImpl::ParseConv },</div> |
| <div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  { <span class="stringliteral">"Add"</span>, &OnnxParserImpl::ParseAdd },</div> |
| <div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  { <span class="stringliteral">"Flatten"</span>, &OnnxParserImpl::ParseFlatten },</div> |
| <div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  { <span class="stringliteral">"Shape"</span>, &OnnxParserImpl::ParseShape },</div> |
| <div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  { <span class="stringliteral">"Gather"</span>, &OnnxParserImpl::ParseGather },</div> |
| <div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  { <span class="stringliteral">"Unsqueeze"</span>, &OnnxParserImpl::ParseUnsqueeze },</div> |
| <div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  { <span class="stringliteral">"Concat"</span>, &OnnxParserImpl::ParseConcat },</div> |
| <div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  { <span class="stringliteral">"Gemm"</span>, &OnnxParserImpl::ParseGemm }</div> |
| <div class="line"><a name="l00493"></a><span class="lineno"> 493</span> };</div> |
| <div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  </div> |
| <div class="line"><a name="l00495"></a><span class="lineno"> 495</span> <span class="keyword">template</span><<span class="keyword">typename</span> TypePair, <span class="keyword">typename</span> Location></div> |
| <div class="line"><a name="l00496"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a74e612d0e7242695de575fb44e7f0762"> 496</a></span> <span class="keywordtype">void</span> OnnxParserImpl::ValidateInputs(<span class="keyword">const</span> onnx::NodeProto& node,</div> |
| <div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  TypePair validInputs,</div> |
| <div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="keyword">const</span> Location& location)</div> |
| <div class="line"><a name="l00499"></a><span class="lineno"> 499</span> {</div> |
| <div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> input : node.input())</div> |
| <div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  {</div> |
| <div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  CheckValidDataType(validInputs.second,</div> |
| <div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  m_TensorsInfo[input].m_dtype,</div> |
| <div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  validInputs.first,</div> |
| <div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  node.name(),</div> |
| <div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  input,</div> |
| <div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  location);</div> |
| <div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  }</div> |
| <div class="line"><a name="l00509"></a><span class="lineno"> 509</span> }</div> |
| <div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  </div> |
| <div class="line"><a name="l00511"></a><span class="lineno"><a class="line" href="_onnx_parser_8cpp.html#a0e987f9d4f46b35c9b1ff0cc950dc5b1"> 511</a></span> <span class="preprocessor">#define VALID_INPUTS(NODE, VALID_INPUTS) \</span></div> |
| <div class="line"><a name="l00512"></a><span class="lineno"> 512</span> <span class="preprocessor"> OnnxParserImpl::ValidateInputs(NODE, \</span></div> |
| <div class="line"><a name="l00513"></a><span class="lineno"> 513</span> <span class="preprocessor"> VALID_INPUTS, \</span></div> |
| <div class="line"><a name="l00514"></a><span class="lineno"> 514</span> <span class="preprocessor"> CHECK_LOCATION())</span></div> |
| <div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  </div> |
| <div class="line"><a name="l00516"></a><span class="lineno"> 516</span> std::vector<TensorInfo> OnnxParserImpl::ComputeOutputInfo(std::vector<std::string> outNames,</div> |
| <div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer,</div> |
| <div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  std::vector<TensorShape> inputShapes,</div> |
| <div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a>& dataType)</div> |
| <div class="line"><a name="l00520"></a><span class="lineno"> 520</span> {</div> |
| <div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <span class="keywordflow">if</span> (outNames.empty())</div> |
| <div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  {</div> |
| <div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">armnn::ParseException</a>(fmt::format(<span class="stringliteral">"Output names are empty {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  }</div> |
| <div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  </div> |
| <div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <span class="keywordtype">bool</span> needCompute = std::any_of(outNames.begin(),</div> |
| <div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  outNames.end(),</div> |
| <div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  [<span class="keyword">this</span>](std::string name)</div> |
| <div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  {</div> |
| <div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  return (m_TensorsInfo.count(name) == 0 ||</div> |
| <div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  m_TensorsInfo[name].m_info == nullptr ||</div> |
| <div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  m_TensorsInfo[name].m_info->GetShape().GetDimensionality() ==</div> |
| <div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  Dimensionality::NotSpecified);</div> |
| <div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  });</div> |
| <div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  std::vector<TensorInfo> outInfo;</div> |
| <div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <span class="comment">//if the output info(s) are not here, we need to compute them</span></div> |
| <div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  std::vector<TensorShape> inferredShapes;</div> |
| <div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> armnnType = DataType::Float32;</div> |
| <div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="keywordflow">if</span>(needCompute) {</div> |
| <div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  inferredShapes = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#aa6e3c075c888e7c16942a468a3aae33c">InferOutputShapes</a>(inputShapes);</div> |
| <div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <span class="keywordflow">if</span> (inferredShapes.size() != outNames.size())</div> |
| <div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  {</div> |
| <div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">armnn::ParseException</a>(fmt::format(<span class="stringliteral">"Inferred shapes does not match number of output names {}"</span>,</div> |
| <div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  }</div> |
| <div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="keywordflow">switch</span> (dataType) {</div> |
| <div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="keywordflow">case</span> onnx::TensorProto::FLOAT: {</div> |
| <div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  armnnType = DataType::Float32;</div> |
| <div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="keywordflow">break</span>;</div> |
| <div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  }</div> |
| <div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <span class="keywordflow">case</span> onnx::TensorProto::INT32:</div> |
| <div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keywordflow">case</span> onnx::TensorProto::INT64: {</div> |
| <div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  armnnType = DataType::Signed32;</div> |
| <div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <span class="keywordflow">break</span>;</div> |
| <div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  }</div> |
| <div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="keywordflow">default</span>: {</div> |
| <div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  fmt::format(<span class="stringliteral">"'{}' is not a currently supported datatype for {}."</span></div> |
| <div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <span class="stringliteral">" Supported dataTypes are FLOAT, INT32 and INT64. {}"</span>,</div> |
| <div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  onnx::TensorProto::DataType_Name(<span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a><span class="keyword">></span>(dataType)),</div> |
| <div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>(),</div> |
| <div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  }</div> |
| <div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  }</div> |
| <div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  }</div> |
| <div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <span class="keywordflow">for</span> (uint i = 0; i < outNames.size(); ++i)</div> |
| <div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  {</div> |
| <div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <span class="keywordflow">if</span>(needCompute)</div> |
| <div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  {</div> |
| <div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  m_TensorsInfo[outNames[i]] = OnnxTensor();</div> |
| <div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  m_TensorsInfo[outNames[i]].m_info = std::make_unique<TensorInfo>(</div> |
| <div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>(inferredShapes[i], armnnType));</div> |
| <div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  m_TensorsInfo[outNames[i]].m_dtype = dataType;</div> |
| <div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  }</div> |
| <div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  outInfo.push_back(*m_TensorsInfo[outNames[i]].m_info);</div> |
| <div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  }</div> |
| <div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="keywordflow">return</span> outInfo;</div> |
| <div class="line"><a name="l00578"></a><span class="lineno"> 578</span> }</div> |
| <div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  </div> |
| <div class="line"><a name="l00580"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#ad131103003f2f4c6e4e3a7406192ad30"> 580</a></span> OnnxParserImpl::OnnxParserImpl()</div> |
| <div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  : m_Network(nullptr, nullptr)</div> |
| <div class="line"><a name="l00582"></a><span class="lineno"> 582</span> {</div> |
| <div class="line"><a name="l00583"></a><span class="lineno"> 583</span> }</div> |
| <div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  </div> |
| <div class="line"><a name="l00585"></a><span class="lineno"> 585</span> <span class="keywordtype">void</span> OnnxParserImpl::ResetParser()</div> |
| <div class="line"><a name="l00586"></a><span class="lineno"> 586</span> {</div> |
| <div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  m_Network = <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a>(<span class="keyword">nullptr</span>, <span class="keyword">nullptr</span>);</div> |
| <div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  m_Graph = <span class="keyword">nullptr</span>;</div> |
| <div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  m_InputInfos.clear();</div> |
| <div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  m_OutputInfos.clear();</div> |
| <div class="line"><a name="l00591"></a><span class="lineno"> 591</span> }</div> |
| <div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  </div> |
| <div class="line"><a name="l00593"></a><span class="lineno"> 593</span> <span class="keywordtype">void</span> OnnxParserImpl::Cleanup()</div> |
| <div class="line"><a name="l00594"></a><span class="lineno"> 594</span> {</div> |
| <div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  m_TensorConnections.clear();</div> |
| <div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  m_TensorsInfo.clear();</div> |
| <div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  m_OutputsMap.clear();</div> |
| <div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  m_OutputsFusedAndUsed.clear();</div> |
| <div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  m_InputShapes.clear();</div> |
| <div class="line"><a name="l00600"></a><span class="lineno"> 600</span> }</div> |
| <div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  </div> |
| <div class="line"><a name="l00602"></a><span class="lineno"> 602</span> <span class="keyword">template</span><<span class="keyword">typename</span> T></div> |
| <div class="line"><a name="l00603"></a><span class="lineno"> 603</span> std::pair<armnn::ConstTensor, std::unique_ptr<T[]>></div> |
| <div class="line"><a name="l00604"></a><span class="lineno"><a class="line" href="namespacearmnn_onnx_parser.html#af6e5ebe4434a071057653025c4bb821b"> 604</a></span> <a class="code" href="namespacearmnn_onnx_parser.html#af6e5ebe4434a071057653025c4bb821b">CreateConstTensorImpl</a>(<span class="keyword">const</span> T* bufferPtr,</div> |
| <div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>& tensorInfo,</div> |
| <div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.html">armnn::Optional<armnn::PermutationVector&></a> permutationVector)</div> |
| <div class="line"><a name="l00607"></a><span class="lineno"> 607</span> {</div> |
| <div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="keywordflow">if</span> (bufferPtr == <span class="keyword">nullptr</span>)</div> |
| <div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  {</div> |
| <div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">armnn::ParseException</a>(fmt::format(<span class="stringliteral">"Buffer for permutation is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  }</div> |
| <div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  </div> |
| <div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  std::unique_ptr<T[]> data(<span class="keyword">new</span> T[tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>()]);</div> |
| <div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  </div> |
| <div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keywordflow">if</span> (permutationVector.<a class="code" href="classarmnn_1_1_optional_base.html#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() && permutationVector.<a class="code" href="classarmnn_1_1_optional_reference_switch.html#a77c7d528ac063d870b8c8426ec81c1c3">value</a>().<a class="code" href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>() > 0)</div> |
| <div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  {</div> |
| <div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  tensorInfo = <a class="code" href="namespacearmnn_utils.html#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(tensorInfo, permutationVector.<a class="code" href="classarmnn_1_1_optional_reference_switch.html#a77c7d528ac063d870b8c8426ec81c1c3">value</a>());</div> |
| <div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <a class="code" href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), permutationVector.<a class="code" href="classarmnn_1_1_optional_reference_switch.html#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(),</div> |
| <div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span>T*<span class="keyword">></span>(bufferPtr), data.get(), <span class="keyword">sizeof</span>(T));</div> |
| <div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  }</div> |
| <div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  {</div> |
| <div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  ::memcpy(data.get(), bufferPtr, tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div> |
| <div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  }</div> |
| <div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  </div> |
| <div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <span class="keywordflow">return</span> std::make_pair(<a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a>(tensorInfo, data.get()), std::move(data));</div> |
| <div class="line"><a name="l00627"></a><span class="lineno"> 627</span> }</div> |
| <div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  </div> |
| <div class="line"><a name="l00629"></a><span class="lineno"> 629</span> std::pair<ConstTensor, std::unique_ptr<float[]>></div> |
| <div class="line"><a name="l00630"></a><span class="lineno"> 630</span> OnnxParserImpl::CreateConstTensor(<span class="keyword">const</span> std::string name,</div> |
| <div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <a class="code" href="classarmnn_1_1_optional.html">armnn::Optional<armnn::PermutationVector&></a> permutationVector)</div> |
| <div class="line"><a name="l00632"></a><span class="lineno"> 632</span> {</div> |
| <div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> tensorInfo = *m_TensorsInfo[name].m_info;</div> |
| <div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  onnx::TensorProto onnxTensor = *m_TensorsInfo[name].m_tensor;</div> |
| <div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  </div> |
| <div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <span class="comment">//ONNX can have Float16 and double constant nodes but ArmNN only supports float32</span></div> |
| <div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <a class="code" href="_onnx_parser_8cpp.html#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a>(name, onnxTensor.name(),</div> |
| <div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a><span class="keyword">></span>(onnxTensor.data_type()), onnx::TensorProto::FLOAT);</div> |
| <div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  </div> |
| <div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="comment">// Makes sure IsConstant flag is set.</span></div> |
| <div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div> |
| <div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  </div> |
| <div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <span class="comment">// Const tensors requires at least a list of values</span></div> |
| <div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <span class="keywordflow">if</span> (tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() == 0)</div> |
| <div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  {</div> |
| <div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"No tensor data found for Const tensor '{}' {}"</span>,</div> |
| <div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  name,</div> |
| <div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  }</div> |
| <div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  </div> |
| <div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <span class="keyword">auto</span> srcData = onnxTensor.float_data().data();</div> |
| <div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <span class="comment">// Copy the value list entries into the destination</span></div> |
| <div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="keywordflow">if</span> (!onnxTensor.has_raw_data())</div> |
| <div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  {</div> |
| <div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <span class="keywordflow">if</span>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() != <span class="keyword">static_cast<</span>uint<span class="keyword">></span>(onnxTensor.float_data_size()))</div> |
| <div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  {</div> |
| <div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  fmt::format(<span class="stringliteral">"The number of data provided ({}) does not match the tensor '{}' number of "</span></div> |
| <div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="stringliteral">"elements ({}) {}"</span>,</div> |
| <div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  onnxTensor.float_data_size(),</div> |
| <div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  name,</div> |
| <div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(),</div> |
| <div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  }</div> |
| <div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <span class="keywordflow">return</span> CreateConstTensorImpl<float>(srcData, tensorInfo, permutationVector);</div> |
| <div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  }</div> |
| <div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  {</div> |
| <div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  <span class="keywordflow">return</span> CreateConstTensorImpl<float>(<span class="keyword">reinterpret_cast<</span><span class="keyword">const </span><span class="keywordtype">float</span>*<span class="keyword">></span>(onnxTensor.raw_data().c_str()),</div> |
| <div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  tensorInfo,</div> |
| <div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  permutationVector);</div> |
| <div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  }</div> |
| <div class="line"><a name="l00673"></a><span class="lineno"> 673</span> }</div> |
| <div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  </div> |
| <div class="line"><a name="l00675"></a><span class="lineno"> 675</span> std::pair<ConstTensor, std::unique_ptr<int32_t[]>></div> |
| <div class="line"><a name="l00676"></a><span class="lineno"> 676</span> OnnxParserImpl::CreateInt64ConstTensor(<span class="keyword">const</span> std::string name,</div> |
| <div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <a class="code" href="classarmnn_1_1_optional.html">armnn::Optional<armnn::PermutationVector&></a> permutationVector)</div> |
| <div class="line"><a name="l00678"></a><span class="lineno"> 678</span> {</div> |
| <div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> tensorInfo = *m_TensorsInfo[name].m_info;</div> |
| <div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  onnx::TensorProto onnxTensor = *m_TensorsInfo[name].m_tensor;</div> |
| <div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  </div> |
| <div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <a class="code" href="_onnx_parser_8cpp.html#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a>(name, onnxTensor.name(),</div> |
| <div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a><span class="keyword">></span>(onnxTensor.data_type()), onnx::TensorProto::INT64);</div> |
| <div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  </div> |
| <div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  <span class="comment">// Makes sure IsConstant flag is set.</span></div> |
| <div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div> |
| <div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  uint numElements = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>();</div> |
| <div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  </div> |
| <div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <span class="comment">// Const tensors requires at least a list of values</span></div> |
| <div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  <span class="keywordflow">if</span> (numElements == 0)</div> |
| <div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  {</div> |
| <div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"No tensor data found for Const tensor '{}' {}"</span>,</div> |
| <div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  name,</div> |
| <div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  }</div> |
| <div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  </div> |
| <div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <span class="comment">// Copy the value list entries into the destination</span></div> |
| <div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="keywordflow">if</span> (!onnxTensor.has_raw_data())</div> |
| <div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  {</div> |
| <div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <span class="keyword">auto</span> srcData = onnxTensor.int64_data().data();</div> |
| <div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  <span class="keywordflow">if</span>(numElements != <span class="keyword">static_cast<</span>uint<span class="keyword">></span>(onnxTensor.int64_data_size()))</div> |
| <div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  {</div> |
| <div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  fmt::format(<span class="stringliteral">"The number of data provided ({}) does not match the tensor '{}' number of "</span></div> |
| <div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="stringliteral">"elements ({}) {}"</span>,</div> |
| <div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  onnxTensor.int64_data_size(),</div> |
| <div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  name,</div> |
| <div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(),</div> |
| <div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  }</div> |
| <div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  </div> |
| <div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  std::vector<int32_t> int32Data;</div> |
| <div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keywordflow">for</span>(uint i = 0; i < numElements; i++)</div> |
| <div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  {</div> |
| <div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  int32_t int32Value = <a class="code" href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(srcData[i]);</div> |
| <div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  int32Data.push_back(int32Value);</div> |
| <div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  }</div> |
| <div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  </div> |
| <div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  <span class="keywordflow">return</span> CreateConstTensorImpl<int32_t>(int32Data.data(), tensorInfo, permutationVector);</div> |
| <div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  }</div> |
| <div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  {</div> |
| <div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <span class="keyword">auto</span> srcData = <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span>int64_t*<span class="keyword">></span>(onnxTensor.raw_data().c_str());</div> |
| <div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  std::vector<int32_t> int32Data;</div> |
| <div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  <span class="keywordflow">for</span>(uint i = 0; i < numElements; i++)</div> |
| <div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  {</div> |
| <div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  int32_t int32Value = <a class="code" href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(srcData[i]);</div> |
| <div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  int32Data.push_back(int32Value);</div> |
| <div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  }</div> |
| <div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <span class="keywordflow">return</span> CreateConstTensorImpl<int32_t>(int32Data.data(), tensorInfo, permutationVector);</div> |
| <div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  }</div> |
| <div class="line"><a name="l00732"></a><span class="lineno"> 732</span> }</div> |
| <div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  </div> |
| <div class="line"><a name="l00734"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a975a79b9b35d51ea81c42c05d245e7c0"> 734</a></span> <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a975a79b9b35d51ea81c42c05d245e7c0">OnnxParserImpl::LoadModelFromTextFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div> |
| <div class="line"><a name="l00735"></a><span class="lineno"> 735</span> {</div> |
| <div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  FILE* fd = fopen(graphFile, <span class="stringliteral">"r"</span>);</div> |
| <div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  </div> |
| <div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  <span class="keywordflow">if</span> (fd == <span class="keyword">nullptr</span>)</div> |
| <div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  {</div> |
| <div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.html">FileNotFoundException</a>(fmt::format(<span class="stringliteral">"Invalid (null) filename {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  }</div> |
| <div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  </div> |
| <div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <span class="comment">// Parse the file into a message</span></div> |
| <div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = std::make_unique<onnx::ModelProto>();</div> |
| <div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <span class="keyword">using</span> google::protobuf::io::FileInputStream;</div> |
| <div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  std::unique_ptr<FileInputStream> input = std::make_unique<FileInputStream>(fileno(fd));</div> |
| <div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <span class="keywordtype">bool</span> success = google::protobuf::TextFormat::Parse(input.get(), modelProto.get());</div> |
| <div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  fclose(fd);</div> |
| <div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  </div> |
| <div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="keywordflow">if</span> (!success)</div> |
| <div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  {</div> |
| <div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  std::stringstream <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>;</div> |
| <div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a> << <span class="stringliteral">"Failed to parse graph file"</span>;</div> |
| <div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"{} {}"</span>, <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>.str(), <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  }</div> |
| <div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <span class="keywordflow">return</span> modelProto;</div> |
| <div class="line"><a name="l00757"></a><span class="lineno"> 757</span> }</div> |
| <div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  </div> |
| <div class="line"><a name="l00759"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aaf4ce461aa35597cf80954314a3cb0e1"> 759</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aaf4ce461aa35597cf80954314a3cb0e1">OnnxParserImpl::CreateNetworkFromTextFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div> |
| <div class="line"><a name="l00760"></a><span class="lineno"> 760</span> {</div> |
| <div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  ResetParser();</div> |
| <div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a975a79b9b35d51ea81c42c05d245e7c0">LoadModelFromTextFile</a>(graphFile);</div> |
| <div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  <span class="keywordflow">return</span> CreateNetworkFromModel(*modelProto);</div> |
| <div class="line"><a name="l00764"></a><span class="lineno"> 764</span> }</div> |
| <div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  </div> |
| <div class="line"><a name="l00766"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a47020c5078f437e9e7a966fcdb42ea30"> 766</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aaf4ce461aa35597cf80954314a3cb0e1">OnnxParserImpl::CreateNetworkFromTextFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div> |
| <div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes)</div> |
| <div class="line"><a name="l00768"></a><span class="lineno"> 768</span> {</div> |
| <div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  ResetParser();</div> |
| <div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  m_InputShapes = inputShapes;</div> |
| <div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a975a79b9b35d51ea81c42c05d245e7c0">LoadModelFromTextFile</a>(graphFile);</div> |
| <div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  <span class="keywordflow">return</span> CreateNetworkFromModel(*modelProto);</div> |
| <div class="line"><a name="l00773"></a><span class="lineno"> 773</span> }</div> |
| <div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  </div> |
| <div class="line"><a name="l00775"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a32a96909bc8a8ee9076bd4d5c1028301"> 775</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a32a96909bc8a8ee9076bd4d5c1028301">OnnxParserImpl::CreateNetworkFromBinary</a>(<span class="keyword">const</span> std::vector<uint8_t>& binaryContent)</div> |
| <div class="line"><a name="l00776"></a><span class="lineno"> 776</span> {</div> |
| <div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  ResetParser();</div> |
| <div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a8e30b9dff215c314959ca3145e939338">LoadModelFromBinary</a>(binaryContent);</div> |
| <div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  <span class="keywordflow">return</span> CreateNetworkFromModel(*modelProto);</div> |
| <div class="line"><a name="l00780"></a><span class="lineno"> 780</span> }</div> |
| <div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  </div> |
| <div class="line"><a name="l00782"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#afe2f6ff5cc92c32d24f5c2f3bf2c8ae8"> 782</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a32a96909bc8a8ee9076bd4d5c1028301">OnnxParserImpl::CreateNetworkFromBinary</a>(<span class="keyword">const</span> std::vector<uint8_t>& binaryContent,</div> |
| <div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes)</div> |
| <div class="line"><a name="l00784"></a><span class="lineno"> 784</span> {</div> |
| <div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  ResetParser();</div> |
| <div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  m_InputShapes = inputShapes;</div> |
| <div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a8e30b9dff215c314959ca3145e939338">LoadModelFromBinary</a>(binaryContent);</div> |
| <div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <span class="keywordflow">return</span> CreateNetworkFromModel(*modelProto);</div> |
| <div class="line"><a name="l00789"></a><span class="lineno"> 789</span> }</div> |
| <div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  </div> |
| <div class="line"><a name="l00791"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a8e30b9dff215c314959ca3145e939338"> 791</a></span> <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a8e30b9dff215c314959ca3145e939338">OnnxParserImpl::LoadModelFromBinary</a>(<span class="keyword">const</span> std::vector<uint8_t>& binaryContent)</div> |
| <div class="line"><a name="l00792"></a><span class="lineno"> 792</span> {</div> |
| <div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <span class="keywordflow">if</span> (binaryContent.size() == 0)</div> |
| <div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  {</div> |
| <div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Missing binary content"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  }</div> |
| <div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <span class="comment">// Parse the file into a message</span></div> |
| <div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = std::make_unique<onnx::ModelProto>();</div> |
| <div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  </div> |
| <div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  google::protobuf::io::CodedInputStream codedStream(binaryContent.data(), <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(binaryContent.size()));</div> |
| <div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  codedStream.SetTotalBytesLimit(INT_MAX);</div> |
| <div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <span class="keywordtype">bool</span> success = modelProto.get()->ParseFromCodedStream(&codedStream);</div> |
| <div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  </div> |
| <div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  <span class="keywordflow">if</span> (!success)</div> |
| <div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  {</div> |
| <div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  std::stringstream <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>;</div> |
| <div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a> << <span class="stringliteral">"Failed to parse graph"</span>;</div> |
| <div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"{} {}"</span>, <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>.str(), <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  }</div> |
| <div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  <span class="keywordflow">return</span> modelProto;</div> |
| <div class="line"><a name="l00811"></a><span class="lineno"> 811</span> }</div> |
| <div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  </div> |
| <div class="line"><a name="l00813"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#acf9c6119ceb99755bc1f86c5a325c796"> 813</a></span> <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#acf9c6119ceb99755bc1f86c5a325c796">OnnxParserImpl::LoadModelFromBinaryFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div> |
| <div class="line"><a name="l00814"></a><span class="lineno"> 814</span> {</div> |
| <div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  FILE* fd = fopen(graphFile, <span class="stringliteral">"rb"</span>);</div> |
| <div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  </div> |
| <div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <span class="keywordflow">if</span> (fd == <span class="keyword">nullptr</span>)</div> |
| <div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  {</div> |
| <div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.html">FileNotFoundException</a>(fmt::format(<span class="stringliteral">"Invalid (null) filename {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  }</div> |
| <div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  </div> |
| <div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  <span class="comment">// Parse the file into a message</span></div> |
| <div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = std::make_unique<onnx::ModelProto>();</div> |
| <div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  </div> |
| <div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  google::protobuf::io::FileInputStream inStream(fileno(fd));</div> |
| <div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  google::protobuf::io::CodedInputStream codedStream(&inStream);</div> |
| <div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  codedStream.SetTotalBytesLimit(INT_MAX);</div> |
| <div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="keywordtype">bool</span> success = modelProto.get()->ParseFromCodedStream(&codedStream);</div> |
| <div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  fclose(fd);</div> |
| <div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  </div> |
| <div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  <span class="keywordflow">if</span> (!success)</div> |
| <div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  {</div> |
| <div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  std::stringstream <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>;</div> |
| <div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a> << <span class="stringliteral">"Failed to parse graph file"</span>;</div> |
| <div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"{} {}"</span>, <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>.str(), <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  }</div> |
| <div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  <span class="keywordflow">return</span> modelProto;</div> |
| <div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  </div> |
| <div class="line"><a name="l00839"></a><span class="lineno"> 839</span> }</div> |
| <div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  </div> |
| <div class="line"><a name="l00841"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aed935c554e4f6a4e7b9dcde057d00877"> 841</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aed935c554e4f6a4e7b9dcde057d00877">OnnxParserImpl::CreateNetworkFromBinaryFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div> |
| <div class="line"><a name="l00842"></a><span class="lineno"> 842</span> {</div> |
| <div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  ResetParser();</div> |
| <div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#acf9c6119ceb99755bc1f86c5a325c796">LoadModelFromBinaryFile</a>(graphFile);</div> |
| <div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <span class="keywordflow">return</span> CreateNetworkFromModel(*modelProto);</div> |
| <div class="line"><a name="l00846"></a><span class="lineno"> 846</span> }</div> |
| <div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  </div> |
| <div class="line"><a name="l00848"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a430131420f20d32b8ae2281c51dcb3ae"> 848</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aed935c554e4f6a4e7b9dcde057d00877">OnnxParserImpl::CreateNetworkFromBinaryFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div> |
| <div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes)</div> |
| <div class="line"><a name="l00850"></a><span class="lineno"> 850</span> {</div> |
| <div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  ResetParser();</div> |
| <div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  m_InputShapes = inputShapes;</div> |
| <div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#acf9c6119ceb99755bc1f86c5a325c796">LoadModelFromBinaryFile</a>(graphFile);</div> |
| <div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <span class="keywordflow">return</span> CreateNetworkFromModel(*modelProto);</div> |
| <div class="line"><a name="l00855"></a><span class="lineno"> 855</span> }</div> |
| <div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  </div> |
| <div class="line"><a name="l00857"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a181f87cf45fdc9f040a41c985ce7f8cd"> 857</a></span> <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a181f87cf45fdc9f040a41c985ce7f8cd">OnnxParserImpl::LoadModelFromString</a>(<span class="keyword">const</span> std::string& protoText)</div> |
| <div class="line"><a name="l00858"></a><span class="lineno"> 858</span> {</div> |
| <div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  <span class="keywordflow">if</span> (protoText == <span class="stringliteral">""</span>)</div> |
| <div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  {</div> |
| <div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.html">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"Invalid (empty) string for model parameter {}"</span>,</div> |
| <div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  }</div> |
| <div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  <span class="comment">// Parse the string into a message</span></div> |
| <div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = std::make_unique<onnx::ModelProto>();</div> |
| <div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  <span class="keywordtype">bool</span> success = google::protobuf::TextFormat::ParseFromString(protoText, modelProto.get());</div> |
| <div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <span class="keywordflow">if</span> (!success)</div> |
| <div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  {</div> |
| <div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  std::stringstream <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>;</div> |
| <div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a> << <span class="stringliteral">"Failed to parse graph file"</span>;</div> |
| <div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"{} {}"</span>, <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>.str(), <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  }</div> |
| <div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <span class="keywordflow">return</span> modelProto;</div> |
| <div class="line"><a name="l00874"></a><span class="lineno"> 874</span> }</div> |
| <div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  </div> |
| <div class="line"><a name="l00876"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a30c0c947bb15e86ee6d535ecd934c0a6"> 876</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a30c0c947bb15e86ee6d535ecd934c0a6">OnnxParserImpl::CreateNetworkFromString</a>(<span class="keyword">const</span> std::string& protoText)</div> |
| <div class="line"><a name="l00877"></a><span class="lineno"> 877</span> {</div> |
| <div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  ResetParser();</div> |
| <div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a181f87cf45fdc9f040a41c985ce7f8cd">LoadModelFromString</a>(protoText);</div> |
| <div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  <span class="keywordflow">return</span> CreateNetworkFromModel(*modelProto);</div> |
| <div class="line"><a name="l00881"></a><span class="lineno"> 881</span> }</div> |
| <div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  </div> |
| <div class="line"><a name="l00883"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a375b0e662129d894cd7627e90f1007cc"> 883</a></span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a30c0c947bb15e86ee6d535ecd934c0a6">OnnxParserImpl::CreateNetworkFromString</a>(<span class="keyword">const</span> std::string& protoText,</div> |
| <div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes)</div> |
| <div class="line"><a name="l00885"></a><span class="lineno"> 885</span> {</div> |
| <div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  ResetParser();</div> |
| <div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  m_InputShapes = inputShapes;</div> |
| <div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  <a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a181f87cf45fdc9f040a41c985ce7f8cd">LoadModelFromString</a>(protoText);</div> |
| <div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  <span class="keywordflow">return</span> CreateNetworkFromModel(*modelProto);</div> |
| <div class="line"><a name="l00890"></a><span class="lineno"> 890</span> }</div> |
| <div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  </div> |
| <div class="line"><a name="l00892"></a><span class="lineno"> 892</span> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> OnnxParserImpl::CreateNetworkFromModel(onnx::ModelProto& model)</div> |
| <div class="line"><a name="l00893"></a><span class="lineno"> 893</span> {</div> |
| <div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  m_Network = INetwork::Create();</div> |
| <div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  <span class="keywordflow">try</span></div> |
| <div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  {</div> |
| <div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  m_Graph = std::make_unique<onnx::GraphProto>(*model.mutable_graph());</div> |
| <div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  LoadGraph();</div> |
| <div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  }</div> |
| <div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>& e)</div> |
| <div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  {</div> |
| <div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  Cleanup();</div> |
| <div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  <span class="keywordflow">throw</span> e;</div> |
| <div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  }</div> |
| <div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  Cleanup();</div> |
| <div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  <span class="keywordflow">return</span> std::move(m_Network);</div> |
| <div class="line"><a name="l00907"></a><span class="lineno"> 907</span> }</div> |
| <div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  </div> |
| <div class="line"><a name="l00909"></a><span class="lineno"> 909</span> <span class="keywordtype">void</span> OnnxParserImpl::LoadGraph()</div> |
| <div class="line"><a name="l00910"></a><span class="lineno"> 910</span> {</div> |
| <div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  <span class="keywordflow">if</span> (m_Graph.get() == <span class="keyword">nullptr</span>)</div> |
| <div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  {</div> |
| <div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">armnn::ParseException</a>(fmt::format(<span class="stringliteral">"Graph pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  }</div> |
| <div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  </div> |
| <div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  <span class="comment">//Fill m_TensorsInfo with the shapes and value of every tensor</span></div> |
| <div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  SetupInfo(m_Graph->mutable_output());</div> |
| <div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  SetupInfo(m_Graph->mutable_input());</div> |
| <div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  SetupInfo(m_Graph->mutable_value_info());</div> |
| <div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  </div> |
| <div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> tensor : m_Graph->initializer())</div> |
| <div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  {</div> |
| <div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  m_TensorsInfo[tensor.name()].m_tensor = std::make_unique<const onnx::TensorProto>(tensor);</div> |
| <div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  m_TensorsInfo[tensor.name()].m_info = std::make_unique<TensorInfo>(<a class="code" href="namespacearmnn_deserializer.html#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(tensor));</div> |
| <div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  m_TensorsInfo[tensor.name()].m_dtype =</div> |
| <div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a><span class="keyword">></span>(tensor.data_type());</div> |
| <div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  }</div> |
| <div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  </div> |
| <div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  SetupInputLayers();</div> |
| <div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  SetupOutputLayers();</div> |
| <div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  </div> |
| <div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  <span class="comment">//Detect FullyConnected layers with bias and update the FusedAndUsed map acccordingly</span></div> |
| <div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  DetectFullyConnected();</div> |
| <div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  </div> |
| <div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  <span class="comment">//Parsing the graph</span></div> |
| <div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> nodeIndex = 0; nodeIndex < static_cast<size_t>(m_Graph->node_size()); nodeIndex++)</div> |
| <div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  {</div> |
| <div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <span class="keyword">auto</span> node = m_Graph->node(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(nodeIndex));</div> |
| <div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <span class="keyword">const</span> std::string& operation = node.op_type();</div> |
| <div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  </div> |
| <div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <span class="comment">// check which layers we handled already (add and matmul fused as FC)</span></div> |
| <div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  <span class="keywordflow">if</span> (operation == <span class="stringliteral">"MatMul"</span> )</div> |
| <div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  {</div> |
| <div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  <span class="keywordflow">if</span>(m_OutputsFusedAndUsed[nodeIndex].inputForNodes != m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.size())</div> |
| <div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  {</div> |
| <div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  <span class="comment">//Node which can not be fused as a FullyConnected layer (used in layers as a simple matmul output)</span></div> |
| <div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  AddFullyConnected(node);</div> |
| <div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  }</div> |
| <div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  }</div> |
| <div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (!(m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.empty()) && operation == <span class="stringliteral">"Add"</span>)</div> |
| <div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  {</div> |
| <div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  <span class="keywordtype">int</span> matmulIndex = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span> (m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes[0]);</div> |
| <div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  AddFullyConnected(m_Graph->node(matmulIndex), &node);</div> |
| <div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  }</div> |
| <div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.empty()) <span class="comment">//node is not part of a fused layer</span></div> |
| <div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  {</div> |
| <div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  <span class="keyword">auto</span> it = m_ParserFunctions.find(operation);</div> |
| <div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  <span class="keywordflow">if</span> (it != m_ParserFunctions.end())</div> |
| <div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  {</div> |
| <div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  <span class="keyword">auto</span> func = it->second;</div> |
| <div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  (this->*func)(node);</div> |
| <div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  }</div> |
| <div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  {</div> |
| <div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Unsupported operation {} for node '{}' {}"</span>,</div> |
| <div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  operation,</div> |
| <div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  node.name(),</div> |
| <div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  }</div> |
| <div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  }</div> |
| <div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  }</div> |
| <div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  </div> |
| <div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  <span class="comment">//Making the connections between outputs and inputs of each layers</span></div> |
| <div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& tensorCon : m_TensorConnections)</div> |
| <div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  {</div> |
| <div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  <span class="keywordflow">if</span> (tensorCon.second.outputSlot != <span class="keyword">nullptr</span>)</div> |
| <div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  {</div> |
| <div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> inputSlotIdx = 0; inputSlotIdx < tensorCon.second.inputSlots.size(); ++inputSlotIdx)</div> |
| <div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  {</div> |
| <div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  tensorCon.second.outputSlot->Connect(*(tensorCon.second.inputSlots[inputSlotIdx]));</div> |
| <div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  }</div> |
| <div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  }</div> |
| <div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  }</div> |
| <div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  </div> |
| <div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <span class="comment">// Get output info.</span></div> |
| <div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> outputIndex = 0; outputIndex < m_Graph->output_size(); ++outputIndex)</div> |
| <div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  {</div> |
| <div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  <span class="keyword">auto</span> output = m_Graph->output(outputIndex);</div> |
| <div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  m_OutputInfos[output.name()] = *m_TensorsInfo[output.name()].m_info;</div> |
| <div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  }</div> |
| <div class="line"><a name="l00991"></a><span class="lineno"> 991</span> }</div> |
| <div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  </div> |
| <div class="line"><a name="l00993"></a><span class="lineno"> 993</span> <span class="keywordtype">void</span> OnnxParserImpl::SetupInfo(<span class="keyword">const</span> google::protobuf::RepeatedPtrField<onnx::ValueInfoProto >* list)</div> |
| <div class="line"><a name="l00994"></a><span class="lineno"> 994</span> {</div> |
| <div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> tensor : *list)</div> |
| <div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  {</div> |
| <div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  m_TensorsInfo[tensor.name()] = OnnxTensor();</div> |
| <div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  m_TensorsInfo[tensor.name()].m_info = std::make_unique<TensorInfo>(<a class="code" href="namespacearmnn_deserializer.html#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(tensor));</div> |
| <div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  m_TensorsInfo[tensor.name()].m_dtype =</div> |
| <div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a><span class="keyword">></span>(tensor.type().tensor_type().elem_type());</div> |
| <div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  }</div> |
| <div class="line"><a name="l01002"></a><span class="lineno"> 1002</span> }</div> |
| <div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  </div> |
| <div class="line"><a name="l01004"></a><span class="lineno"> 1004</span> <span class="keywordtype">void</span> OnnxParserImpl::DetectFullyConnected()</div> |
| <div class="line"><a name="l01005"></a><span class="lineno"> 1005</span> {</div> |
| <div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  m_OutputsFusedAndUsed = std::vector<UsageSummary> (<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(m_Graph->node_size()), UsageSummary());</div> |
| <div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  <span class="keyword">auto</span> matmulAndConstant = [&](<span class="keyword">const</span> std::string& constInput,</div> |
| <div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  <span class="keyword">const</span> std::string& matmulInput,</div> |
| <div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  <span class="keywordtype">int</span>& nodeIndex)</div> |
| <div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  {</div> |
| <div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  <span class="keyword">auto</span> matmulIt = m_OutputsMap.find(matmulInput);</div> |
| <div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  <span class="keywordflow">if</span>(matmulIt != m_OutputsMap.end() && matmulIt->second.first->op_type() == <span class="stringliteral">"MatMul"</span></div> |
| <div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  && m_TensorsInfo[constInput].isConstant())</div> |
| <div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  {</div> |
| <div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  nodeIndex = matmulIt->second.second;</div> |
| <div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div> |
| <div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  }</div> |
| <div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div> |
| <div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  };</div> |
| <div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  </div> |
| <div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> nodeIndex = 0; nodeIndex < m_Graph->node_size(); nodeIndex++)</div> |
| <div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  {</div> |
| <div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  <span class="keyword">const</span> onnx::NodeProto* node = &m_Graph->node(nodeIndex);</div> |
| <div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string& output : node->output())</div> |
| <div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  {</div> |
| <div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  m_OutputsMap[output] = std::make_pair(node, nodeIndex);</div> |
| <div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  }</div> |
| <div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  </div> |
| <div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string& input : node->input()) <span class="comment">//count how many time a node is used as input</span></div> |
| <div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  {</div> |
| <div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  <span class="keyword">auto</span> matmulIt = m_OutputsMap.find(input);</div> |
| <div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  <span class="keywordflow">if</span>(matmulIt != m_OutputsMap.end()){</div> |
| <div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  ++m_OutputsFusedAndUsed[<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(matmulIt->second.second)].inputForNodes; <span class="comment">//node used</span></div> |
| <div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  }</div> |
| <div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  }</div> |
| <div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  </div> |
| <div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  <span class="keywordflow">if</span> (node->op_type() == <span class="stringliteral">"Add"</span>)</div> |
| <div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  {</div> |
| <div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  <span class="keywordtype">int</span> matmulIndex = 0;</div> |
| <div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  <span class="keywordflow">if</span> (matmulAndConstant(node->input(0), node->input(1), matmulIndex) ||</div> |
| <div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  matmulAndConstant(node->input(1), node->input(0), matmulIndex))</div> |
| <div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  {</div> |
| <div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  <span class="comment">//matmul and add were fused</span></div> |
| <div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  m_OutputsFusedAndUsed[<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(matmulIndex)].fusedWithNodes</div> |
| <div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  .push_back(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(nodeIndex));</div> |
| <div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  </div> |
| <div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  m_OutputsFusedAndUsed[<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(nodeIndex)].fusedWithNodes</div> |
| <div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  .push_back(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(matmulIndex));</div> |
| <div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  }</div> |
| <div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  }</div> |
| <div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  }</div> |
| <div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  </div> |
| <div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> output: m_Graph->output()) { <span class="comment">//Add usages as output of the graph in count of usages</span></div> |
| <div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  <span class="keyword">auto</span> matmulIt = m_OutputsMap.find(output.name());</div> |
| <div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <span class="keywordflow">if</span>(matmulIt != m_OutputsMap.end()){</div> |
| <div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  ++m_OutputsFusedAndUsed[<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(matmulIt->second.second)].inputForNodes;</div> |
| <div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  }</div> |
| <div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  }</div> |
| <div class="line"><a name="l01059"></a><span class="lineno"> 1059</span> }</div> |
| <div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  </div> |
| <div class="line"><a name="l01061"></a><span class="lineno"> 1061</span> <span class="keyword">template</span><<span class="keyword">typename</span> Location></div> |
| <div class="line"><a name="l01062"></a><span class="lineno"> 1062</span> <span class="keywordtype">void</span> OnnxParserImpl::GetInputAndParam(<span class="keyword">const</span> onnx::NodeProto& node,</div> |
| <div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  std::string* inputName,</div> |
| <div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  std::string* constName,</div> |
| <div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  <span class="keyword">const</span> Location& location)</div> |
| <div class="line"><a name="l01066"></a><span class="lineno"> 1066</span> {</div> |
| <div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  <span class="keywordtype">int</span> cstIndex;</div> |
| <div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  <span class="keywordflow">if</span> (m_TensorsInfo[node.input(0)].isConstant())</div> |
| <div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  {</div> |
| <div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  cstIndex = 0;</div> |
| <div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  }</div> |
| <div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (m_TensorsInfo[node.input(1)].isConstant())</div> |
| <div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  {</div> |
| <div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  cstIndex = 1;</div> |
| <div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  }</div> |
| <div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  {</div> |
| <div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"One of the input tensors ('{}' or '{}') should be constant in node '{}' {}"</span>,</div> |
| <div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  node.input(0),</div> |
| <div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  node.input(1),</div> |
| <div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  node.name(),</div> |
| <div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  location.AsString()));</div> |
| <div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  }</div> |
| <div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  <span class="keywordflow">if</span>(constName)</div> |
| <div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  {</div> |
| <div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  *constName = node.input(cstIndex);</div> |
| <div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  }</div> |
| <div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  <span class="keywordflow">if</span>(inputName)</div> |
| <div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  {</div> |
| <div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  *inputName = node.input(!cstIndex);</div> |
| <div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  }</div> |
| <div class="line"><a name="l01092"></a><span class="lineno"> 1092</span> }</div> |
| <div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  </div> |
| <div class="line"><a name="l01094"></a><span class="lineno"> 1094</span> <span class="keyword">template</span><<span class="keyword">typename</span> Location></div> |
| <div class="line"><a name="l01095"></a><span class="lineno"> 1095</span> <span class="keywordtype">void</span> OnnxParserImpl::To1DTensor(<span class="keyword">const</span> std::string& name, <span class="keyword">const</span> Location& location)</div> |
| <div class="line"><a name="l01096"></a><span class="lineno"> 1096</span> {</div> |
| <div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> shape = m_TensorsInfo[name].m_info->GetShape();</div> |
| <div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  std::vector<uint32_t> newShape;</div> |
| <div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  <span class="keywordflow">for</span>(uint i = 0; i < shape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - 1; ++i)</div> |
| <div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  {</div> |
| <div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  <span class="keywordflow">if</span>(shape[i] != 1)</div> |
| <div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  {</div> |
| <div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  fmt::format(<span class="stringliteral">"Only tensors with shape [1, ..., 1, X] can be converted to 1D and {} {}"</span>,</div> |
| <div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  TensorInfoAsString(*m_TensorsInfo[name].m_info, name, m_TensorsInfo[name].m_dtype),</div> |
| <div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  location.AsString()));</div> |
| <div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  }</div> |
| <div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  }</div> |
| <div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  newShape.push_back(shape[shape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - 1]);</div> |
| <div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  </div> |
| <div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  m_TensorsInfo[name].m_info->SetShape(<a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>(<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(newShape.size()), newShape.data()));</div> |
| <div class="line"><a name="l01112"></a><span class="lineno"> 1112</span> }</div> |
| <div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  </div> |
| <div class="line"><a name="l01114"></a><span class="lineno"> 1114</span> <span class="keywordtype">void</span> OnnxParserImpl::AddConvLayerWithDepthwiseConv(<span class="keyword">const</span> onnx::NodeProto& node, <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convolution2d_descriptor.html">Convolution2dDescriptor</a>& convDesc)</div> |
| <div class="line"><a name="l01115"></a><span class="lineno"> 1115</span> {</div> |
| <div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  <a class="code" href="_assert_8hpp.html#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(node.op_type() == <span class="stringliteral">"Conv"</span>);</div> |
| <div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  </div> |
| <div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html">DepthwiseConvolution2dDescriptor</a> desc;</div> |
| <div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>;</div> |
| <div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>;</div> |
| <div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>;</div> |
| <div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>;</div> |
| <div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>;</div> |
| <div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>;</div> |
| <div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>;</div> |
| <div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  </div> |
| <div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">armnn::IConnectableLayer</a>* layer = m_Network->AddDepthwiseConvolution2dLayer(desc, node.name().c_str());</div> |
| <div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  std::string permuteStr = <span class="stringliteral">"permute_"</span> + node.input(1);</div> |
| <div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  std::vector<std::string> tensorIndexes= {node.input(0), permuteStr};</div> |
| <div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  </div> |
| <div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  <span class="keyword">auto</span> weightTensor = CreateConstTensor(node.input(1));</div> |
| <div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* weightsLayer = m_Network->AddConstantLayer(weightTensor.first);</div> |
| <div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  </div> |
| <div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  <span class="comment">// weights come in as [O,1,H,W] from ONNX and need to be converted to ArmNNs depthwise weights layout [1,H,W,O]</span></div> |
| <div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  <a class="code" href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a> perVec {3, 0, 1, 2};</div> |
| <div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> weightsPermuted = <a class="code" href="namespacearmnn_utils.html#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(weightTensor.first.GetInfo(), perVec);</div> |
| <div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  </div> |
| <div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  <span class="comment">// Inserts NewLayer so layers don't need to be re-sorted.</span></div> |
| <div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* permuteLayer = m_Network->AddPermuteLayer(<a class="code" href="structarmnn_1_1_permute_descriptor.html">PermuteDescriptor</a>(perVec),</div> |
| <div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  <span class="stringliteral">"permute_layer"</span>);</div> |
| <div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  permuteLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(weightsPermuted);</div> |
| <div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  permuteLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1u));</div> |
| <div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  </div> |
| <div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(weightTensor.first.GetInfo());</div> |
| <div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(permuteLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0u));</div> |
| <div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  </div> |
| <div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  <span class="keywordflow">if</span> (node.input_size() == 3)</div> |
| <div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  {</div> |
| <div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  <span class="keywordflow">if</span>(!m_TensorsInfo[node.input(2)].isConstant())</div> |
| <div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  {</div> |
| <div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Bias '{}' should be constant in Conv layer '{}' {}"</span>,</div> |
| <div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  node.input(2),</div> |
| <div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  node.name(),</div> |
| <div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  }</div> |
| <div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  </div> |
| <div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div> |
| <div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  <span class="keyword">auto</span> biasTensor = CreateConstTensor(node.input(2));</div> |
| <div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  tensorIndexes.emplace_back(node.input(2));</div> |
| <div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  </div> |
| <div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* biasLayer = m_Network->AddConstantLayer(biasTensor.first);</div> |
| <div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  biasLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(biasTensor.first.GetInfo());</div> |
| <div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  biasLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2u));</div> |
| <div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  }</div> |
| <div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  </div> |
| <div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  {</div> |
| <div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  }</div> |
| <div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  </div> |
| <div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({ node.output(0) }, layer,</div> |
| <div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  { m_TensorsInfo[node.input(0)].m_info->GetShape(),</div> |
| <div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  weightsPermuted.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() });</div> |
| <div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  </div> |
| <div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  </div> |
| <div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div> |
| <div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  RegisterInputSlots(layer, tensorIndexes);</div> |
| <div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  </div> |
| <div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  RegisterOutputSlots(layer, {node.output(0)});</div> |
| <div class="line"><a name="l01183"></a><span class="lineno"> 1183</span> }</div> |
| <div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  </div> |
| <div class="line"><a name="l01185"></a><span class="lineno"> 1185</span> <span class="keywordtype">void</span> OnnxParserImpl::AddFullyConnected(<span class="keyword">const</span> onnx::NodeProto& matmulNode, <span class="keyword">const</span> onnx::NodeProto* addNode)</div> |
| <div class="line"><a name="l01186"></a><span class="lineno"> 1186</span> {</div> |
| <div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  <span class="comment">// find matmul inputs</span></div> |
| <div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  std::string inputName;</div> |
| <div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  std::string weightName;</div> |
| <div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  std::string biasName;</div> |
| <div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  std::string outputName;</div> |
| <div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(matmulNode.input_size()), 2);</div> |
| <div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(matmulNode.output_size()), 1);</div> |
| <div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  <a class="code" href="_onnx_parser_8cpp.html#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(matmulNode, <a class="code" href="_onnx_parser_8cpp.html#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</div> |
| <div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  </div> |
| <div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  GetInputAndParam(matmulNode, &inputName, &weightName, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div> |
| <div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  </div> |
| <div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> inputInfo = *m_TensorsInfo[inputName].m_info;</div> |
| <div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> weightInfo = *m_TensorsInfo[weightName].m_info;</div> |
| <div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> biasInfo;</div> |
| <div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  </div> |
| <div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  std::vector<std::string> inputNames;</div> |
| <div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  </div> |
| <div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.html">FullyConnectedDescriptor</a> desc;</div> |
| <div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = addNode != <span class="keyword">nullptr</span>;</div> |
| <div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  </div> |
| <div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = <span class="keyword">nullptr</span>;</div> |
| <div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  <span class="keywordflow">if</span>(desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div> |
| <div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  {</div> |
| <div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  <span class="comment">// find bias const</span></div> |
| <div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(addNode->input_size()), 2);</div> |
| <div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(addNode->output_size()), 1);</div> |
| <div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  <a class="code" href="_onnx_parser_8cpp.html#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(*addNode, <a class="code" href="_onnx_parser_8cpp.html#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</div> |
| <div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  </div> |
| <div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  GetInputAndParam(*addNode, <span class="keyword">nullptr</span>, &biasName, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div> |
| <div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  </div> |
| <div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  <span class="comment">//Output shape is [1, weights[1]] and 1d vec in ONNX can be [1,X] so we convert biases to "armnn" 1D</span></div> |
| <div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  To1DTensor(biasName, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div> |
| <div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  biasInfo = *m_TensorsInfo[biasName].m_info;</div> |
| <div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  </div> |
| <div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  <span class="keywordflow">if</span> (weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1] != biasInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0])</div> |
| <div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  {</div> |
| <div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  fmt::format(<span class="stringliteral">"Shape of weights '{}' and bias of following Add node '{}' do not match : {}"</span></div> |
| <div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  <span class="stringliteral">" and {} ( /!\\ bias should be a 1D tensor) {}"</span>,</div> |
| <div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  weightName,</div> |
| <div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  addNode->name(),</div> |
| <div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  TensorInfoAsString(*m_TensorsInfo[weightName].m_info, weightName,</div> |
| <div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  m_TensorsInfo[weightName].m_dtype),</div> |
| <div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  TensorInfoAsString(*m_TensorsInfo[biasName].m_info, biasName,</div> |
| <div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  m_TensorsInfo[biasName].m_dtype ),</div> |
| <div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  }</div> |
| <div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  </div> |
| <div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  inputNames = { inputName, weightName, biasName };</div> |
| <div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  outputName = addNode->output(0);</div> |
| <div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  }</div> |
| <div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  {</div> |
| <div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  inputNames = { inputName, weightName };</div> |
| <div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  outputName = matmulNode.output(0);</div> |
| <div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  }</div> |
| <div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  </div> |
| <div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  <span class="comment">// Just add a FullyConnected layer, weights and biases are handled as inputs now.</span></div> |
| <div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  layer = m_Network->AddFullyConnectedLayer(desc, matmulNode.name().c_str());</div> |
| <div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  </div> |
| <div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  {</div> |
| <div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  }</div> |
| <div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  </div> |
| <div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  <span class="keywordflow">if</span> (inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() > 2)</div> |
| <div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  {</div> |
| <div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  <span class="comment">// Add reshape to flatten to 2D [batch_size, input_size],</span></div> |
| <div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  <span class="comment">// where "input_size" corresponds to the number of inputs to the layer,</span></div> |
| <div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <span class="comment">// matching the second dimension of weights,</span></div> |
| <div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  <span class="comment">// and "batch_size" is calculated by dividing the number of elements by "input_size".</span></div> |
| <div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  std::vector<unsigned int> reshapedDimensions(2);</div> |
| <div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  reshapedDimensions[1] = weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</div> |
| <div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  reshapedDimensions[0] = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / reshapedDimensions[1];</div> |
| <div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>  </div> |
| <div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  <span class="keywordflow">if</span> (inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() % reshapedDimensions[1] != 0)</div> |
| <div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  {</div> |
| <div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  fmt::format(<span class="stringliteral">"Failed to deduce input tensor shape from filter size {} {}"</span>,</div> |
| <div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>  reshapedDimensions[1],</div> |
| <div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  }</div> |
| <div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  </div> |
| <div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> reshapedTensorInfo = inputInfo;</div> |
| <div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a>{ 2, reshapedDimensions.data() });</div> |
| <div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  inputInfo = reshapedTensorInfo;</div> |
| <div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  </div> |
| <div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.html">ReshapeDescriptor</a> reshapeDescriptor;</div> |
| <div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  reshapeDescriptor.<a class="code" href="structarmnn_1_1_reshape_descriptor.html#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div> |
| <div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  </div> |
| <div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  std::string reshapeLayerName = fmt::format(<span class="stringliteral">"Reshape_for:{}"</span>, layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>());</div> |
| <div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* reshapeLayer = m_Network->AddReshapeLayer(reshapeDescriptor, reshapeLayerName.c_str());</div> |
| <div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  </div> |
| <div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(reshapedTensorInfo);</div> |
| <div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div> |
| <div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  </div> |
| <div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  RegisterInputSlots(reshapeLayer, {inputName});</div> |
| <div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  inputNames[0] = reshapeLayerName;</div> |
| <div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  }</div> |
| <div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  </div> |
| <div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({ outputName },</div> |
| <div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  layer,</div> |
| <div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  { inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(),</div> |
| <div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() });</div> |
| <div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  </div> |
| <div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  RegisterInputSlots(layer, inputNames);</div> |
| <div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  </div> |
| <div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  <span class="comment">// Add constant layer to store weights/biases and connect to FullyConnected layer..</span></div> |
| <div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  <span class="keywordflow">if</span>(m_TensorsInfo[weightName].isConstant())</div> |
| <div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>  {</div> |
| <div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* weightsLayer = m_Network->AddConstantLayer(CreateConstTensor(weightName).first);</div> |
| <div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  </div> |
| <div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div> |
| <div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(weightInfo);</div> |
| <div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1u));</div> |
| <div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>  }</div> |
| <div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  </div> |
| <div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  <span class="keywordflow">if</span>(desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> && m_TensorsInfo[biasName].isConstant())</div> |
| <div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  {</div> |
| <div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* biasLayer = m_Network->AddConstantLayer(CreateConstTensor(biasName).first);</div> |
| <div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  </div> |
| <div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  biasInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div> |
| <div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  biasLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(biasInfo);</div> |
| <div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  biasLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2u));</div> |
| <div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  }</div> |
| <div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  </div> |
| <div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  <span class="keywordflow">if</span> (outputInfo[0].GetNumDimensions() > 2)</div> |
| <div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>  {</div> |
| <div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  <span class="comment">// Calculate reshape to flatten to 2D [batch_size, input_size]</span></div> |
| <div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  std::vector<unsigned int> reshapedDimensions(2);</div> |
| <div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  reshapedDimensions[1] = weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div> |
| <div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  reshapedDimensions[0] = outputInfo[0].<a class="code" href="classarmnn_1_1_tensor_shape.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / reshapedDimensions[1];</div> |
| <div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  </div> |
| <div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  <span class="keywordflow">if</span> (outputInfo[0].GetNumElements() % reshapedDimensions[1] != 0)</div> |
| <div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  {</div> |
| <div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  fmt::format(<span class="stringliteral">"Failed to deduce output tensor shape from filter size {} {}"</span>,</div> |
| <div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  reshapedDimensions[1],</div> |
| <div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  }</div> |
| <div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  </div> |
| <div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> reshapedOutputTensorInfo = outputInfo[0];</div> |
| <div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  reshapedOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a>{ 2, reshapedDimensions.data() });</div> |
| <div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(reshapedOutputTensorInfo);</div> |
| <div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  </div> |
| <div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.html">ReshapeDescriptor</a> desc;</div> |
| <div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  desc.<a class="code" href="structarmnn_1_1_reshape_descriptor.html#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = outputInfo[0].GetShape();</div> |
| <div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  </div> |
| <div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  std::string reshapeLayerName = fmt::format(<span class="stringliteral">"ExpandDims_for:{}"</span>, layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>());</div> |
| <div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* reshapeLayer = m_Network->AddReshapeLayer(desc, reshapeLayerName.c_str());</div> |
| <div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  </div> |
| <div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div> |
| <div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  </div> |
| <div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>  RegisterInputSlots(reshapeLayer, {layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>()});</div> |
| <div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>  layer = reshapeLayer;</div> |
| <div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>  }</div> |
| <div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  </div> |
| <div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>  RegisterOutputSlots(layer, { outputName });</div> |
| <div class="line"><a name="l01347"></a><span class="lineno"> 1347</span> }</div> |
| <div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  </div> |
| <div class="line"><a name="l01349"></a><span class="lineno"> 1349</span> <span class="keywordtype">void</span> OnnxParserImpl::AddPoolingLayer(<span class="keyword">const</span> onnx::NodeProto& node, <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">Pooling2dDescriptor</a>& desc)</div> |
| <div class="line"><a name="l01350"></a><span class="lineno"> 1350</span> {</div> |
| <div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  </div> |
| <div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.input_size()), 1);</div> |
| <div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.output_size()), 1);</div> |
| <div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>  </div> |
| <div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  <a class="code" href="_onnx_parser_8cpp.html#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(node, <a class="code" href="_onnx_parser_8cpp.html#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</div> |
| <div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  </div> |
| <div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  std::vector<uint32_t> kernel_shape = ReadMandatoryNodeUint32ListAttribute(node, <span class="stringliteral">"kernel_shape"</span>); <span class="comment">//size of pool win</span></div> |
| <div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  std::vector<uint32_t> strides = ReadOptionalNodeUint32ListAttribute(node, <span class="stringliteral">"strides"</span>);</div> |
| <div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  std::vector<uint32_t> pads = ReadOptionalNodeUint32ListAttribute(node, <span class="stringliteral">"pads"</span>);</div> |
| <div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  </div> |
| <div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = OutputShapeRounding::Floor;</div> |
| <div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = kernel_shape[1];</div> |
| <div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = kernel_shape[0];</div> |
| <div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>  </div> |
| <div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>  <span class="keywordflow">if</span>(strides.empty())</div> |
| <div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  {</div> |
| <div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div> |
| <div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div> |
| <div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>  }</div> |
| <div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>  {</div> |
| <div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strides[1];</div> |
| <div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strides[0];</div> |
| <div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>  }</div> |
| <div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  </div> |
| <div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  <span class="comment">//Check new padding version first</span></div> |
| <div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>  <span class="keywordflow">if</span>(pads.empty())</div> |
| <div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  {</div> |
| <div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  <span class="comment">//Check deprecated version</span></div> |
| <div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  std::string paddingString = ReadOptionalNodeStringAttribute(node, <span class="stringliteral">"auto_pad"</span>);</div> |
| <div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>  <span class="keywordflow">if</span>(paddingString != <span class="stringliteral">"VALID"</span> && paddingString != <span class="stringliteral">""</span> && paddingString != <span class="stringliteral">"NOTSET"</span>)</div> |
| <div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  {</div> |
| <div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>  <span class="keywordtype">bool</span> isUpper;</div> |
| <div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>  <span class="keywordflow">if</span>( paddingString == <span class="stringliteral">"SAME_LOWER"</span>)</div> |
| <div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  {</div> |
| <div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>  isUpper = <span class="keyword">false</span>;</div> |
| <div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  }</div> |
| <div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (paddingString == <span class="stringliteral">"SAME_UPPER"</span>)</div> |
| <div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>  {</div> |
| <div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  isUpper = <span class="keyword">true</span>;</div> |
| <div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  }</div> |
| <div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  {</div> |
| <div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Invalid auto_pad attribute for node {}. "</span></div> |
| <div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  <span class="stringliteral">"Only SAME_UPPER, SAME_LOWER or VALID supported and found {} {}"</span>,</div> |
| <div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  node.name(),</div> |
| <div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  paddingString,</div> |
| <div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>  }</div> |
| <div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  <span class="keyword">auto</span> inputInfo = *m_TensorsInfo[node.input(0)].m_info;</div> |
| <div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  uint32_t inputHeight = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div> |
| <div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  uint32_t inputWidth = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3];</div> |
| <div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  CalcPadding(inputHeight,</div> |
| <div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a>,</div> |
| <div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>,</div> |
| <div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  1u,</div> |
| <div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  &desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>,</div> |
| <div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  &desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>,</div> |
| <div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>  isUpper);</div> |
| <div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>  CalcPadding(inputWidth,</div> |
| <div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>,</div> |
| <div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>,</div> |
| <div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>  1u,</div> |
| <div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>  &desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>,</div> |
| <div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  &desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>,</div> |
| <div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>  isUpper);</div> |
| <div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  }</div> |
| <div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>  }</div> |
| <div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  {</div> |
| <div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = pads[0];</div> |
| <div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = pads[1];</div> |
| <div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = pads[2];</div> |
| <div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = pads[3];</div> |
| <div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  }</div> |
| <div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  </div> |
| <div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = m_Network->AddPooling2dLayer(desc, node.name().c_str());</div> |
| <div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  </div> |
| <div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  {</div> |
| <div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>  }</div> |
| <div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  </div> |
| <div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()});</div> |
| <div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  </div> |
| <div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div> |
| <div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>  RegisterInputSlots(layer, {node.input(0)});</div> |
| <div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  </div> |
| <div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  RegisterOutputSlots(layer, {node.output(0)});</div> |
| <div class="line"><a name="l01443"></a><span class="lineno"> 1443</span> }</div> |
| <div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  </div> |
| <div class="line"><a name="l01445"></a><span class="lineno"> 1445</span> std::pair<std::string, std::string> OnnxParserImpl::AddPrepareBroadcast(<span class="keyword">const</span> std::string& input0,</div> |
| <div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  <span class="keyword">const</span> std::string& input1)</div> |
| <div class="line"><a name="l01447"></a><span class="lineno"> 1447</span> {</div> |
| <div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>  std::pair<std::string, std::string> inputs = std::make_pair(input0, input1);</div> |
| <div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>  </div> |
| <div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> input0Shape = m_TensorsInfo[input0].m_info->GetShape();</div> |
| <div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> input1Shape = m_TensorsInfo[input1].m_info->GetShape();</div> |
| <div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  </div> |
| <div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>  <span class="keywordflow">if</span>(input1Shape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() < input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div> |
| <div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>  {</div> |
| <div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>  <span class="keyword">auto</span> outputName = fmt::format(<span class="stringliteral">"reshape_output_{}"</span>, input1);</div> |
| <div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>  PrependForBroadcast(outputName, input1, input0);</div> |
| <div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  inputs.second = outputName;</div> |
| <div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  }</div> |
| <div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() < input1Shape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div> |
| <div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>  {</div> |
| <div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  <span class="keyword">auto</span> outputName = fmt::format(<span class="stringliteral">"reshape_output_{}"</span>, input0);</div> |
| <div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>  PrependForBroadcast(outputName, input0, input1);</div> |
| <div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  inputs.first = outputName;</div> |
| <div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>  }</div> |
| <div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>  <span class="keywordflow">return</span> inputs;</div> |
| <div class="line"><a name="l01466"></a><span class="lineno"> 1466</span> }</div> |
| <div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>  </div> |
| <div class="line"><a name="l01468"></a><span class="lineno"> 1468</span> <span class="keywordtype">void</span> OnnxParserImpl::CreateConstantLayer(<span class="keyword">const</span> std::string& tensorName, <span class="keyword">const</span> std::string& layerName)</div> |
| <div class="line"><a name="l01469"></a><span class="lineno"> 1469</span> {</div> |
| <div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>  <span class="keyword">auto</span> armnnTensor = CreateConstTensor(tensorName);</div> |
| <div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = m_Network->AddConstantLayer(armnnTensor.first, layerName.c_str());</div> |
| <div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(armnnTensor.first.GetInfo());</div> |
| <div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>  RegisterOutputSlots(layer, {tensorName});</div> |
| <div class="line"><a name="l01474"></a><span class="lineno"> 1474</span> }</div> |
| <div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>  </div> |
| <div class="line"><a name="l01476"></a><span class="lineno"> 1476</span> <span class="keywordtype">void</span> OnnxParserImpl::CreateInt64ConstantLayer(<span class="keyword">const</span> std::string& tensorName, <span class="keyword">const</span> std::string& layerName)</div> |
| <div class="line"><a name="l01477"></a><span class="lineno"> 1477</span> {</div> |
| <div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  <span class="keyword">auto</span> armnnTensor = CreateInt64ConstTensor(tensorName);</div> |
| <div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = m_Network->AddConstantLayer(armnnTensor.first, layerName.c_str());</div> |
| <div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(armnnTensor.first.GetInfo());</div> |
| <div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>  RegisterOutputSlots(layer, {tensorName});</div> |
| <div class="line"><a name="l01482"></a><span class="lineno"> 1482</span> }</div> |
| <div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  </div> |
| <div class="line"><a name="l01484"></a><span class="lineno"> 1484</span> <span class="keywordtype">void</span> OnnxParserImpl::CreateReshapeLayer(<span class="keyword">const</span> std::string& inputName,</div> |
| <div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>  <span class="keyword">const</span> std::string& outputName,</div> |
| <div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>  <span class="keyword">const</span> std::string& layerName)</div> |
| <div class="line"><a name="l01487"></a><span class="lineno"> 1487</span> {</div> |
| <div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> outputTensorInfo = *m_TensorsInfo[outputName].m_info;</div> |
| <div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.html">ReshapeDescriptor</a> reshapeDesc;</div> |
| <div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.html#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div> |
| <div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  </div> |
| <div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());</div> |
| <div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>  </div> |
| <div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>  {</div> |
| <div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  }</div> |
| <div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  </div> |
| <div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div> |
| <div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  </div> |
| <div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div> |
| <div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  RegisterInputSlots(layer, {inputName});</div> |
| <div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>  </div> |
| <div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l01506"></a><span class="lineno"> 1506</span>  RegisterOutputSlots(layer, {outputName});</div> |
| <div class="line"><a name="l01507"></a><span class="lineno"> 1507</span> }</div> |
| <div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  </div> |
| <div class="line"><a name="l01509"></a><span class="lineno"> 1509</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseActivation(<span class="keyword">const</span> onnx::NodeProto& node, <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a> func)</div> |
| <div class="line"><a name="l01510"></a><span class="lineno"> 1510</span> {</div> |
| <div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.input_size()), 1, 3);</div> |
| <div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.output_size()), 1);</div> |
| <div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>  </div> |
| <div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>  <a class="code" href="_onnx_parser_8cpp.html#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(node, <a class="code" href="_onnx_parser_8cpp.html#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</div> |
| <div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>  </div> |
| <div class="line"><a name="l01516"></a><span class="lineno"> 1516</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.html">ActivationDescriptor</a> desc;</div> |
| <div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  desc.<a class="code" href="structarmnn_1_1_activation_descriptor.html#af10fa7883e3579950f477bee92a64844">m_Function</a> = func;</div> |
| <div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>  </div> |
| <div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  <span class="keywordflow">if</span> (func == ActivationFunction::BoundedReLu)</div> |
| <div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  {</div> |
| <div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  <span class="keywordflow">if</span> (node.input_size() == 1 && node.attribute_size() > 0)</div> |
| <div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>  {</div> |
| <div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  desc.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a017b2990003a014234f13e999dc7c689">m_A</a> = ReadOptionalNodeFloatAttribute(node, <span class="stringliteral">"max"</span>, std::numeric_limits<float>::max());</div> |
| <div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>  desc.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = ReadOptionalNodeFloatAttribute(node, <span class="stringliteral">"min"</span>, std::numeric_limits<float>::lowest());</div> |
| <div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>  }</div> |
| <div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>  {</div> |
| <div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  desc.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a017b2990003a014234f13e999dc7c689">m_A</a> = node.input(2).empty() ? std::numeric_limits<float>::max() : <a class="code" href="namespacestd.html">std</a>::stof(node.input(2));</div> |
| <div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  desc.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = node.input(1).empty() ? std::numeric_limits<float>::lowest() : <a class="code" href="namespacestd.html">std</a>::stof(node.input(1));</div> |
| <div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>  }</div> |
| <div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  }</div> |
| <div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>  </div> |
| <div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network->AddActivationLayer(desc, node.name().c_str());</div> |
| <div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  </div> |
| <div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  {</div> |
| <div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  }</div> |
| <div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  </div> |
| <div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({ node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()});</div> |
| <div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>  </div> |
| <div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div> |
| <div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>  RegisterInputSlots(layer, {node.input(0)});</div> |
| <div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  </div> |
| <div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  RegisterOutputSlots(layer, {node.output(0)});</div> |
| <div class="line"><a name="l01549"></a><span class="lineno"> 1549</span> }</div> |
| <div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>  </div> |
| <div class="line"><a name="l01551"></a><span class="lineno"> 1551</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseClip(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l01552"></a><span class="lineno"> 1552</span> {</div> |
| <div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  ParseActivation(node, ActivationFunction::BoundedReLu);</div> |
| <div class="line"><a name="l01554"></a><span class="lineno"> 1554</span> }</div> |
| <div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  </div> |
| <div class="line"><a name="l01556"></a><span class="lineno"> 1556</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseSigmoid(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l01557"></a><span class="lineno"> 1557</span> {</div> |
| <div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>  ParseActivation(node, ActivationFunction::Sigmoid);</div> |
| <div class="line"><a name="l01559"></a><span class="lineno"> 1559</span> }</div> |
| <div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  </div> |
| <div class="line"><a name="l01561"></a><span class="lineno"> 1561</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseTanh(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l01562"></a><span class="lineno"> 1562</span> {</div> |
| <div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>  ParseActivation(node, ActivationFunction::TanH);</div> |
| <div class="line"><a name="l01564"></a><span class="lineno"> 1564</span> }</div> |
| <div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  </div> |
| <div class="line"><a name="l01566"></a><span class="lineno"> 1566</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseRelu(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l01567"></a><span class="lineno"> 1567</span> {</div> |
| <div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>  ParseActivation(node, ActivationFunction::ReLu);</div> |
| <div class="line"><a name="l01569"></a><span class="lineno"> 1569</span> }</div> |
| <div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  </div> |
| <div class="line"><a name="l01571"></a><span class="lineno"> 1571</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseLeakyRelu(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l01572"></a><span class="lineno"> 1572</span> {</div> |
| <div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  ParseActivation(node, ActivationFunction::LeakyReLu);</div> |
| <div class="line"><a name="l01574"></a><span class="lineno"> 1574</span> }</div> |
| <div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>  </div> |
| <div class="line"><a name="l01576"></a><span class="lineno"> 1576</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseAdd(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l01577"></a><span class="lineno"> 1577</span> {</div> |
| <div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.input_size()), 2);</div> |
| <div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.output_size()), 1);</div> |
| <div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  </div> |
| <div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  <a class="code" href="_onnx_parser_8cpp.html#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(node, <a class="code" href="_onnx_parser_8cpp.html#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</div> |
| <div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>  </div> |
| <div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>  <span class="comment">// IVGCVSW-1576: unify broadcast validation code across layers</span></div> |
| <div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  </div> |
| <div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>  <span class="comment">// Checking broadcast compatibility : only scalar or 1D tensors</span></div> |
| <div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>  <span class="keyword">auto</span> inputs = AddPrepareBroadcast(node.input(0), node.input(1));</div> |
| <div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  <span class="keyword">auto</span> input0 = *m_TensorsInfo[inputs.first].m_info;</div> |
| <div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  <span class="keyword">auto</span> input1 = *m_TensorsInfo[inputs.second].m_info;</div> |
| <div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>  <span class="keywordflow">if</span> (input0.GetNumDimensions() != input1.GetNumDimensions())</div> |
| <div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  {</div> |
| <div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">armnn::ParseException</a>(fmt::format(<span class="stringliteral">"Dimension mismatch in node {} {}"</span>,</div> |
| <div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  node.name(),</div> |
| <div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  }</div> |
| <div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>  </div> |
| <div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDims = input0.GetNumDimensions();</div> |
| <div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numDims; i++)</div> |
| <div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>  {</div> |
| <div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dim0 = input0.GetShape()[i];</div> |
| <div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dim1 = input1.GetShape()[i];</div> |
| <div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>  <span class="keywordflow">if</span> (dim0 != dim1 && dim0 != 1 && dim1 != 1)</div> |
| <div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>  {</div> |
| <div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>  fmt::format(<span class="stringliteral">"Broadcast is only supported for scalar or 1D tensors in Add node '{}'. "</span></div> |
| <div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  <span class="stringliteral">"Input dimensions should either match or one should be of size 1 and here, "</span></div> |
| <div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>  <span class="stringliteral">"{} and {} {}"</span>,</div> |
| <div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  node.name(),</div> |
| <div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>  TensorInfoAsString(*m_TensorsInfo[inputs.first].m_info, inputs.first,</div> |
| <div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>  m_TensorsInfo[inputs.first].m_dtype),</div> |
| <div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  TensorInfoAsString(*m_TensorsInfo[inputs.second].m_info, inputs.second,</div> |
| <div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  m_TensorsInfo[inputs.second].m_dtype),</div> |
| <div class="line"><a name="l01612"></a><span class="lineno"> 1612</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01613"></a><span class="lineno"> 1613</span>  }</div> |
| <div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  }</div> |
| <div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  </div> |
| <div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  </div> |
| <div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = m_Network->AddElementwiseBinaryLayer(BinaryOperation::Add, node.name().c_str());</div> |
| <div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>  </div> |
| <div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>  {</div> |
| <div class="line"><a name="l01621"></a><span class="lineno"> 1621</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>  }</div> |
| <div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  </div> |
| <div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({ node.output(0) }, layer,</div> |
| <div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  { m_TensorsInfo[inputs.first].m_info->GetShape(),</div> |
| <div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  m_TensorsInfo[inputs.second].m_info->GetShape() });</div> |
| <div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>  </div> |
| <div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>  <span class="comment">// register the input connection -> for constant inputs, we need to make a newDim constant layer</span></div> |
| <div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>  <span class="keywordflow">if</span>(m_TensorsInfo[inputs.first].isConstant()) {</div> |
| <div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  CreateConstantLayer(inputs.first, fmt::format(<span class="stringliteral">"Add:constant_of_{}"</span>, node.input(0)));</div> |
| <div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>  }</div> |
| <div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>  <span class="keywordflow">if</span>(m_TensorsInfo[inputs.second].isConstant()) {</div> |
| <div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>  CreateConstantLayer(inputs.second, fmt::format(<span class="stringliteral">"Add:constant_of_{}"</span>, node.input(1)));</div> |
| <div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  }</div> |
| <div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>  RegisterInputSlots(layer, {inputs.first, inputs.second});</div> |
| <div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>  </div> |
| <div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>  <span class="comment">// register the output connection</span></div> |
| <div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>  RegisterOutputSlots(layer, {node.output(0)});</div> |
| <div class="line"><a name="l01640"></a><span class="lineno"> 1640</span> }</div> |
| <div class="line"><a name="l01641"></a><span class="lineno"> 1641</span>  </div> |
| <div class="line"><a name="l01642"></a><span class="lineno"> 1642</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseAveragePool(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l01643"></a><span class="lineno"> 1643</span> {</div> |
| <div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">Pooling2dDescriptor</a> desc;</div> |
| <div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = PoolingAlgorithm::Average;</div> |
| <div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>  </div> |
| <div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  uint32_t count_include_pad = 0;</div> |
| <div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  count_include_pad = ReadOptionalNodeUint32Attribute(node, <span class="stringliteral">"count_include_pad"</span>);</div> |
| <div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>  <span class="keywordflow">if</span>(count_include_pad) {</div> |
| <div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = PaddingMethod::IgnoreValue;</div> |
| <div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  }</div> |
| <div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  AddPoolingLayer(node, desc);</div> |
| <div class="line"><a name="l01653"></a><span class="lineno"> 1653</span> }</div> |
| <div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  </div> |
| <div class="line"><a name="l01655"></a><span class="lineno"> 1655</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseBatchNormalization(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l01656"></a><span class="lineno"> 1656</span> {</div> |
| <div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>  <span class="comment">//IGNORE momentum parameter and spatial parameters</span></div> |
| <div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>  </div> |
| <div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.input_size()), 5);</div> |
| <div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.output_size()), 1);</div> |
| <div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>  </div> |
| <div class="line"><a name="l01662"></a><span class="lineno"> 1662</span>  <a class="code" href="_onnx_parser_8cpp.html#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(node, <a class="code" href="_onnx_parser_8cpp.html#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</div> |
| <div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> ind = 1; ind < node.input_size(); ++ind)</div> |
| <div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>  {</div> |
| <div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  <span class="keyword">auto</span> tensor = node.input(ind);</div> |
| <div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>  <span class="keywordflow">if</span>(! m_TensorsInfo[tensor].isConstant())</div> |
| <div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>  {</div> |
| <div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>  fmt::format(<span class="stringliteral">"Input tensor '{}' should be constant in BatchNormalization node '{}' {}"</span>,</div> |
| <div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>  tensor,</div> |
| <div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>  node.name(),</div> |
| <div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>  }</div> |
| <div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>  }</div> |
| <div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  </div> |
| <div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>  <span class="keywordtype">float</span> epsilon = ReadOptionalNodeFloatAttribute(node, <span class="stringliteral">"epsilon"</span>, 1e-5f);</div> |
| <div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.html">BatchNormalizationDescriptor</a> desc;</div> |
| <div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>  desc.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.html#a11c821c7524251004a72ed13c510853c">m_Eps</a> = epsilon;</div> |
| <div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>  </div> |
| <div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>  <span class="keyword">auto</span> scaleTensor = CreateConstTensor(node.input(1));</div> |
| <div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>  <span class="keyword">auto</span> biasTensor = CreateConstTensor(node.input(2));</div> |
| <div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>  <span class="keyword">auto</span> meanTensor = CreateConstTensor(node.input(3));</div> |
| <div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>  <span class="keyword">auto</span> varTensor = CreateConstTensor(node.input(4));</div> |
| <div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  </div> |
| <div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = m_Network->AddBatchNormalizationLayer(desc,</div> |
| <div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  meanTensor.first,</div> |
| <div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>  varTensor.first,</div> |
| <div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>  biasTensor.first,</div> |
| <div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>  scaleTensor.first,</div> |
| <div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>  node.name().c_str());</div> |
| <div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>  </div> |
| <div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>  {</div> |
| <div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>  }</div> |
| <div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  </div> |
| <div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()});</div> |
| <div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  </div> |
| <div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  RegisterInputSlots(layer, {node.input(0)}); <span class="comment">//don't register constant inputs</span></div> |
| <div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>  </div> |
| <div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  <span class="comment">// register the output connection</span></div> |
| <div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>  RegisterOutputSlots(layer, {node.output(0)});</div> |
| <div class="line"><a name="l01704"></a><span class="lineno"> 1704</span> }</div> |
| <div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>  </div> |
| <div class="line"><a name="l01706"></a><span class="lineno"> 1706</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseConcat(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l01707"></a><span class="lineno"> 1707</span> {</div> |
| <div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.output_size()), 1);</div> |
| <div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>  </div> |
| <div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>  uint32_t numConcatView = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(node.input_size());</div> |
| <div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>  uint32_t inputRank = m_TensorsInfo[node.input(0)].m_info->GetNumDimensions();</div> |
| <div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>  </div> |
| <div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  <span class="keywordtype">int</span> axisInt = ReadMandatoryNodeIntAttribute(node, <span class="stringliteral">"axis"</span>);</div> |
| <div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>  </div> |
| <div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDimInput = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(</div> |
| <div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  (<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(inputRank) + axisInt) % <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(inputRank));</div> |
| <div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  </div> |
| <div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.html">OriginsDescriptor</a> concatDescriptor(numConcatView, inputRank);</div> |
| <div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  concatDescriptor.SetConcatAxis(concatDimInput);</div> |
| <div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  </div> |
| <div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mergeDimOrigin = 0;</div> |
| <div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  </div> |
| <div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  std::vector<TensorShape> inputShapes;</div> |
| <div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>  std::vector<std::string> tensorIds;</div> |
| <div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>  </div> |
| <div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> viewIndex = 0; viewIndex < numConcatView; ++viewIndex)</div> |
| <div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  {</div> |
| <div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  std::string nodeName = node.input(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(viewIndex));</div> |
| <div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  <span class="keyword">auto</span> inputTensorInfo = *m_TensorsInfo[nodeName].m_info;</div> |
| <div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  inputShapes.push_back(inputTensorInfo.GetShape());</div> |
| <div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  tensorIds.push_back(nodeName);</div> |
| <div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>  </div> |
| <div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  <span class="comment">// Set up concatDescriptor view origin</span></div> |
| <div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  <a class="code" href="namespacearmnn_utils.html#a523deabeb7d0a884028b35eebfd1cb6c">armnnUtils::ProcessConcatInputTensorInfo</a>(</div> |
| <div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>  inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin);</div> |
| <div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>  }</div> |
| <div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>  </div> |
| <div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = m_Network->AddConcatLayer(concatDescriptor, node.name().c_str());</div> |
| <div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  </div> |
| <div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  {</div> |
| <div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  }</div> |
| <div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  </div> |
| <div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({node.output(0)}, layer, inputShapes,</div> |
| <div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>  m_TensorsInfo[node.input(0)].m_dtype);</div> |
| <div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  </div> |
| <div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>  </div> |
| <div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>  RegisterInputSlots(layer, tensorIds);</div> |
| <div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>  </div> |
| <div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  RegisterOutputSlots(layer, { node.output(0) });</div> |
| <div class="line"><a name="l01755"></a><span class="lineno"> 1755</span> }</div> |
| <div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>  </div> |
| <div class="line"><a name="l01757"></a><span class="lineno"> 1757</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseConstant(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l01758"></a><span class="lineno"> 1758</span> {</div> |
| <div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.attribute_size()), 1);</div> |
| <div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>  <span class="keywordflow">if</span> (!node.attribute(0).has_t())</div> |
| <div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  {</div> |
| <div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Value not found for Constant node '{}' {}"</span>,</div> |
| <div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  node.name(),</div> |
| <div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>  }</div> |
| <div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>  <span class="keyword">const</span> onnx::TensorProto& onnxTensor = node.attribute(0).t();</div> |
| <div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>  </div> |
| <div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  <span class="comment">//Register this as a m_ConstParam so we know we can use it as a constant param in future layers.</span></div> |
| <div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>  m_TensorsInfo[node.output(0)].m_tensor = std::make_unique<const onnx::TensorProto>(onnxTensor);</div> |
| <div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>  m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(<a class="code" href="namespacearmnn_deserializer.html#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(onnxTensor));</div> |
| <div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>  m_TensorsInfo[node.output(0)].m_dtype = <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a><span class="keyword">></span>(onnxTensor.data_type());</div> |
| <div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>  </div> |
| <div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>  <span class="keywordflow">if</span> (m_TensorsInfo[node.output(0)].m_dtype == onnx::TensorProto_DataType_FLOAT)</div> |
| <div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>  {</div> |
| <div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  CreateConstantLayer(node.output(0), node.name());</div> |
| <div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  }</div> |
| <div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (m_TensorsInfo[node.output(0)].m_dtype == onnx::TensorProto_DataType_INT64)</div> |
| <div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  {</div> |
| <div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  CreateInt64ConstantLayer(node.output(0), node.name());</div> |
| <div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>  }</div> |
| <div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  {</div> |
| <div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Data type not support for Constant node '{}' {}"</span>,</div> |
| <div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>  node.name(),</div> |
| <div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>  }</div> |
| <div class="line"><a name="l01787"></a><span class="lineno"> 1787</span> }</div> |
| <div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>  </div> |
| <div class="line"><a name="l01789"></a><span class="lineno"> 1789</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseConv(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l01790"></a><span class="lineno"> 1790</span> {</div> |
| <div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.input_size()), 2, 3); <span class="comment">//input, weight, (bias)</span></div> |
| <div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.output_size()), 1);</div> |
| <div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  </div> |
| <div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  <a class="code" href="_onnx_parser_8cpp.html#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(node, <a class="code" href="_onnx_parser_8cpp.html#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</div> |
| <div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>  </div> |
| <div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>  <span class="keywordflow">if</span>(m_TensorsInfo[node.input(0)].m_info->GetNumDimensions() != 4)</div> |
| <div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  {</div> |
| <div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>  fmt::format(<span class="stringliteral">"ArmNN only supports 2D convolution and Conv layer '{}' input {} {}"</span>,</div> |
| <div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>  node.name(),</div> |
| <div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  TensorInfoAsString(*m_TensorsInfo[node.input(0)].m_info, node.input(0),</div> |
| <div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>  m_TensorsInfo[node.input(0)].m_dtype),</div> |
| <div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>  }</div> |
| <div class="line"><a name="l01805"></a><span class="lineno"> 1805</span>  </div> |
| <div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>  <span class="keywordflow">if</span>(!m_TensorsInfo[node.input(1)].isConstant())</div> |
| <div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  {</div> |
| <div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  fmt::format(<span class="stringliteral">"Weights '{}' should be constant in Conv layer '{}' {}"</span>,</div> |
| <div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>  node.input(1),</div> |
| <div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  node.name(),</div> |
| <div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>  }</div> |
| <div class="line"><a name="l01814"></a><span class="lineno"> 1814</span>  </div> |
| <div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>  <span class="keyword">auto</span> inputInfo = *m_TensorsInfo[node.input(0)].m_info;</div> |
| <div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>  </div> |
| <div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.html">Convolution2dDescriptor</a> desc;</div> |
| <div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div> |
| <div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>  </div> |
| <div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>  std::vector<uint32_t> strides = ReadOptionalNodeUint32ListAttribute(node, <span class="stringliteral">"strides"</span>);</div> |
| <div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  <span class="keywordflow">if</span>(strides.empty())</div> |
| <div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>  {</div> |
| <div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div> |
| <div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div> |
| <div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  }</div> |
| <div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>  {</div> |
| <div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strides[1];</div> |
| <div class="line"><a name="l01829"></a><span class="lineno"> 1829</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strides[0];</div> |
| <div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>  }</div> |
| <div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>  </div> |
| <div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>  std::vector<uint32_t> dilations = ReadOptionalNodeUint32ListAttribute(node, <span class="stringliteral">"dilations"</span>);</div> |
| <div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>  <span class="keywordflow">if</span>(!dilations.empty())</div> |
| <div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  {</div> |
| <div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = dilations[1];</div> |
| <div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = dilations[0];</div> |
| <div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>  }</div> |
| <div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>  </div> |
| <div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>  std::vector<uint32_t> pads = ReadOptionalNodeUint32ListAttribute(node, <span class="stringliteral">"pads"</span>);</div> |
| <div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  <span class="comment">//Check new padding version first</span></div> |
| <div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>  <span class="keywordflow">if</span>(pads.empty())</div> |
| <div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>  {</div> |
| <div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>  <span class="comment">//Check deprecated version</span></div> |
| <div class="line"><a name="l01844"></a><span class="lineno"> 1844</span>  std::string paddingString = ReadOptionalNodeStringAttribute(node, <span class="stringliteral">"auto_pad"</span>);</div> |
| <div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>  <span class="keywordflow">if</span>(paddingString != <span class="stringliteral">"VALID"</span> && paddingString != <span class="stringliteral">""</span> && paddingString != <span class="stringliteral">"NOTSET"</span>)</div> |
| <div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>  {</div> |
| <div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>  <span class="keywordtype">bool</span> isUpper;</div> |
| <div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>  <span class="keywordflow">if</span>( paddingString == <span class="stringliteral">"SAME_LOWER"</span>)</div> |
| <div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>  {</div> |
| <div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>  isUpper = <span class="keyword">false</span>;</div> |
| <div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>  }</div> |
| <div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (paddingString == <span class="stringliteral">"SAME_UPPER"</span>)</div> |
| <div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>  {</div> |
| <div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>  isUpper = <span class="keyword">true</span>;</div> |
| <div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>  }</div> |
| <div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>  {</div> |
| <div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>  fmt::format(<span class="stringliteral">"Invalid auto_pad attribute for node {}. Only SAME_UPPER, SAME_LOWER or VALID "</span></div> |
| <div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>  <span class="stringliteral">"supported and found {} {}"</span>,</div> |
| <div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>  node.name(),</div> |
| <div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>  paddingString,</div> |
| <div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>  }</div> |
| <div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>  uint32_t inputHeight = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div> |
| <div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>  uint32_t inputWidth = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3];</div> |
| <div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  </div> |
| <div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>  uint32_t weightHeight;</div> |
| <div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>  uint32_t weightWidth;</div> |
| <div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>  std::vector<uint32_t> kernel_shape = ReadOptionalNodeUint32ListAttribute(node, <span class="stringliteral">"kernel_shape"</span>);</div> |
| <div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>  <span class="keywordflow">if</span> (kernel_shape.empty())</div> |
| <div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>  {</div> |
| <div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> weightTensorInfo = *m_TensorsInfo[node.input(1)].m_info;</div> |
| <div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  weightHeight = weightTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div> |
| <div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>  weightWidth = weightTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3];</div> |
| <div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>  }</div> |
| <div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>  {</div> |
| <div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>  weightHeight = kernel_shape[0];</div> |
| <div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>  weightWidth = kernel_shape[1];</div> |
| <div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>  }</div> |
| <div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>  CalcPadding(inputHeight,</div> |
| <div class="line"><a name="l01883"></a><span class="lineno"> 1883</span>  weightHeight,</div> |
| <div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>,</div> |
| <div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a>,</div> |
| <div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>  &desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>,</div> |
| <div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>  &desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>,</div> |
| <div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>  isUpper);</div> |
| <div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>  CalcPadding(inputWidth,</div> |
| <div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>  weightWidth,</div> |
| <div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>,</div> |
| <div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a>,</div> |
| <div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>  &desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>,</div> |
| <div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>  &desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>,</div> |
| <div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  isUpper);</div> |
| <div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>  }</div> |
| <div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>  }</div> |
| <div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>  {</div> |
| <div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = pads[0];</div> |
| <div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = pads[1];</div> |
| <div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = pads[2];</div> |
| <div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = pads[3];</div> |
| <div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>  }</div> |
| <div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>  </div> |
| <div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>  uint32_t group = ReadOptionalNodeUint32Attribute(node, <span class="stringliteral">"group"</span>, 1);</div> |
| <div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>  <span class="keywordflow">if</span>(group > 1)</div> |
| <div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>  {</div> |
| <div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>  <span class="keywordflow">if</span> (group > inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1])</div> |
| <div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>  {</div> |
| <div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>  fmt::format(<span class="stringliteral">"Error parsing Convolution node: {}. "</span></div> |
| <div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>  <span class="stringliteral">"The 'group'={} parameter cannot be larger than the "</span></div> |
| <div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>  <span class="stringliteral">"channel of the input shape={} (in NCHW format). {}"</span>,</div> |
| <div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>  node.name(),</div> |
| <div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>  group,</div> |
| <div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>  inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1],</div> |
| <div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>  }</div> |
| <div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (group == inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1])</div> |
| <div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>  {</div> |
| <div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>  <span class="comment">// we use a depthwise convolution here, because the number of groups equals to the</span></div> |
| <div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>  <span class="comment">// input channels</span></div> |
| <div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>  AddConvLayerWithDepthwiseConv(node, desc);</div> |
| <div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>  <span class="keywordflow">return</span>;</div> |
| <div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>  }</div> |
| <div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>  {</div> |
| <div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Error parsing Convolution node: {}. "</span></div> |
| <div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>  <span class="stringliteral">"The 'group'={} parameter should be 1 or be equal to the "</span></div> |
| <div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>  <span class="stringliteral">"channel of the input shape={} (in NCHW format). {}"</span>,</div> |
| <div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>  node.name(),</div> |
| <div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>  group,</div> |
| <div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>  inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1],</div> |
| <div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>  }</div> |
| <div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>  }</div> |
| <div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>  </div> |
| <div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>  node.input_size() == 3 ? desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = true : desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div> |
| <div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">armnn::IConnectableLayer</a>* layer = m_Network->AddConvolution2dLayer(desc, node.name().c_str());</div> |
| <div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>  std::vector<std::string> tensorIndexes= {node.input(0), node.input(1)};</div> |
| <div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>  </div> |
| <div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>  <span class="keyword">auto</span> weightTensor = CreateConstTensor(node.input(1));</div> |
| <div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>  </div> |
| <div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* weightsLayer = m_Network->AddConstantLayer(weightTensor.first);</div> |
| <div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(weightTensor.first.GetInfo());</div> |
| <div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1u));</div> |
| <div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>  </div> |
| <div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>  <span class="keywordflow">if</span> (node.input_size() == 3)</div> |
| <div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>  {</div> |
| <div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>  <span class="keywordflow">if</span>(!m_TensorsInfo[node.input(2)].isConstant())</div> |
| <div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>  {</div> |
| <div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Bias '{}' should be constant in Conv layer '{}' {}"</span>,</div> |
| <div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>  node.input(2),</div> |
| <div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>  node.name(),</div> |
| <div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>  }</div> |
| <div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div> |
| <div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>  <span class="keyword">auto</span> biasTensor = CreateConstTensor(node.input(2));</div> |
| <div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>  </div> |
| <div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* biasLayer = m_Network->AddConstantLayer(biasTensor.first);</div> |
| <div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>  biasLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(biasTensor.first.GetInfo());</div> |
| <div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>  biasLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2u));</div> |
| <div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>  </div> |
| <div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>  tensorIndexes.emplace_back(node.input(2));</div> |
| <div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>  }</div> |
| <div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>  </div> |
| <div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>  {</div> |
| <div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>  }</div> |
| <div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>  </div> |
| <div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({ node.output(0) }, layer,</div> |
| <div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>  { m_TensorsInfo[node.input(0)].m_info->GetShape(),</div> |
| <div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>  m_TensorsInfo[node.input(1)].m_info->GetShape() });</div> |
| <div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>  </div> |
| <div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div> |
| <div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  RegisterInputSlots(layer, tensorIndexes);</div> |
| <div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>  </div> |
| <div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>  RegisterOutputSlots(layer, {node.output(0)});</div> |
| <div class="line"><a name="l01984"></a><span class="lineno"> 1984</span> }</div> |
| <div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>  </div> |
| <div class="line"><a name="l01986"></a><span class="lineno"> 1986</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseFlatten(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l01987"></a><span class="lineno"> 1987</span> {</div> |
| <div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.input_size()), 1);</div> |
| <div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.output_size()), 1);</div> |
| <div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>  </div> |
| <div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>  <a class="code" href="_onnx_parser_8cpp.html#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a>(node.name(), node.input(0),</div> |
| <div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>  m_TensorsInfo[node.input(0)].m_dtype,</div> |
| <div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>  onnx::TensorProto::FLOAT);</div> |
| <div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>  </div> |
| <div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>  int64_t axis = ReadOptionalNodeInt64Attribute(node, <span class="stringliteral">"axis"</span>, 1);</div> |
| <div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape();</div> |
| <div class="line"><a name="l01997"></a><span class="lineno"> 1997</span> <span class="comment"></span> </div> |
| <div class="line"><a name="l01998"></a><span class="lineno"> 1998</span> <span class="comment"> /// Negative axis conversion</span></div> |
| <div class="line"><a name="l01999"></a><span class="lineno"> 1999</span> <span class="comment"></span> <span class="keywordflow">if</span> (axis < 0)</div> |
| <div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>  {</div> |
| <div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>  axis += inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div> |
| <div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>  }</div> |
| <div class="line"><a name="l02003"></a><span class="lineno"> 2003</span> <span class="comment"></span> </div> |
| <div class="line"><a name="l02004"></a><span class="lineno"> 2004</span> <span class="comment"> /// Check Axis is within dimensions</span></div> |
| <div class="line"><a name="l02005"></a><span class="lineno"> 2005</span> <span class="comment"></span> <span class="keywordflow">if</span> (axis < 0 || axis >= inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div> |
| <div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>  {</div> |
| <div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Axis '{}' invalid. Tensor has '{}' dimensions in FlattenLayer '{}'"</span>,</div> |
| <div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>  axis, inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), node.name()));</div> |
| <div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>  }</div> |
| <div class="line"><a name="l02010"></a><span class="lineno"> 2010</span> <span class="comment"></span> </div> |
| <div class="line"><a name="l02011"></a><span class="lineno"> 2011</span> <span class="comment"> /// If axis chosen is 0 dimension1 will always be 1 in output , default dimension2 to 1 because 0 is invalid</span></div> |
| <div class="line"><a name="l02012"></a><span class="lineno"> 2012</span> <span class="comment"></span> uint dimension1{1};</div> |
| <div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>  uint dimension2{1};</div> |
| <div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>  uint i{0};</div> |
| <div class="line"><a name="l02015"></a><span class="lineno"> 2015</span> <span class="comment"></span> </div> |
| <div class="line"><a name="l02016"></a><span class="lineno"> 2016</span> <span class="comment"> /// dimension1 = (d_0 * d_1 ... d_(axis-1))</span></div> |
| <div class="line"><a name="l02017"></a><span class="lineno"> 2017</span> <span class="comment"></span> <span class="keywordflow">for</span> (i = 0; i < axis; i++){</div> |
| <div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>  dimension1 *= inputShape[i];</div> |
| <div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>  }</div> |
| <div class="line"><a name="l02020"></a><span class="lineno"> 2020</span> <span class="comment"></span> </div> |
| <div class="line"><a name="l02021"></a><span class="lineno"> 2021</span> <span class="comment"> /// dimension2 = (d_axis * d_(axis+1) ... d_n)</span></div> |
| <div class="line"><a name="l02022"></a><span class="lineno"> 2022</span> <span class="comment"></span> <span class="keywordflow">for</span> (i = <span class="keyword">static_cast<</span>uint<span class="keyword">></span>(axis); i < inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++){</div> |
| <div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>  dimension2 *= inputShape[i];</div> |
| <div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>  }</div> |
| <div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>  </div> |
| <div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> outputShape{dimension1, dimension2};</div> |
| <div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>  </div> |
| <div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>  <span class="keyword">auto</span> outInfo = ComputeReshapeInfo(outputShape, inputShape, node.output(0));</div> |
| <div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>  m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(outInfo);</div> |
| <div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>  CreateReshapeLayer(node.input(0), node.output(0), node.name());</div> |
| <div class="line"><a name="l02031"></a><span class="lineno"> 2031</span> }</div> |
| <div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>  </div> |
| <div class="line"><a name="l02033"></a><span class="lineno"> 2033</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseGather(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l02034"></a><span class="lineno"> 2034</span> {</div> |
| <div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.input_size()), 2);</div> |
| <div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.output_size()), 1);</div> |
| <div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>  </div> |
| <div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>  <a class="code" href="structarmnn_1_1_gather_descriptor.html">armnn::GatherDescriptor</a> gatherDescriptor;</div> |
| <div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>  gatherDescriptor.<a class="code" href="structarmnn_1_1_gather_descriptor.html#a35d11c7d509d1adbae1ae01c58394a7f">m_Axis</a> = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(ReadOptionalNodeInt64Attribute(node, <span class="stringliteral">"axis"</span>, 0));</div> |
| <div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>  </div> |
| <div class="line"><a name="l02041"></a><span class="lineno"> 2041</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = m_Network->AddGatherLayer(gatherDescriptor, node.name().c_str());</div> |
| <div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>  </div> |
| <div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>  {</div> |
| <div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>  }</div> |
| <div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>  </div> |
| <div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>& inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape();</div> |
| <div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>& indicesShape = m_TensorsInfo[node.input(1)].m_info->GetShape();</div> |
| <div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({node.output(0)}, layer, { inputShape, indicesShape },</div> |
| <div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>  m_TensorsInfo[node.input(0)].m_dtype);</div> |
| <div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>  </div> |
| <div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>  RegisterInputSlots(layer, { node.input(0), node.input(1) });</div> |
| <div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>  </div> |
| <div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>  RegisterOutputSlots(layer, { node.output(0) });</div> |
| <div class="line"><a name="l02059"></a><span class="lineno"> 2059</span> }</div> |
| <div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>  </div> |
| <div class="line"><a name="l02061"></a><span class="lineno"> 2061</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseGemm(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l02062"></a><span class="lineno"> 2062</span> {</div> |
| <div class="line"><a name="l02063"></a><span class="lineno"> 2063</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.input_size()), 2, 3);</div> |
| <div class="line"><a name="l02064"></a><span class="lineno"> 2064</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.output_size()), 1);</div> |
| <div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>  </div> |
| <div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>  <span class="keywordtype">int</span> transA = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(ReadOptionalNodeUint32Attribute(node, <span class="stringliteral">"transA"</span>, 0));</div> |
| <div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>  <span class="keywordtype">int</span> transB = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(ReadOptionalNodeUint32Attribute(node, <span class="stringliteral">"transB"</span>, 0));</div> |
| <div class="line"><a name="l02068"></a><span class="lineno"> 2068</span>  <span class="keywordtype">float</span> alpha = ReadOptionalNodeFloatAttribute(node, <span class="stringliteral">"alpha"</span>, 1.0);</div> |
| <div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>  <span class="keywordtype">float</span> beta = ReadOptionalNodeFloatAttribute(node, <span class="stringliteral">"beta"</span>, 1.0);</div> |
| <div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  <span class="keywordtype">bool</span> biasEnabled = node.input_size() == 3;</div> |
| <div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>  </div> |
| <div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> input0Shape = m_TensorsInfo[node.input(0)].m_info->GetShape();</div> |
| <div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> input1Shape = m_TensorsInfo[node.input(1)].m_info->GetShape();</div> |
| <div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>  </div> |
| <div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>  <span class="comment">// if transB != 0, add transpose to the input1 (tanspose weight matrix in FullyConnected)</span></div> |
| <div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.html">armnn::FullyConnectedDescriptor</a> fullyConnectedDescriptor;</div> |
| <div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>  fullyConnectedDescriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div> |
| <div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>  fullyConnectedDescriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.html#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = transB;</div> |
| <div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>  </div> |
| <div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = <span class="keyword">nullptr</span>;</div> |
| <div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  </div> |
| <div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>  <span class="comment">// Just add a FullyConnected layer, weights and biases are handled as inputs now.</span></div> |
| <div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>  layer = m_Network->AddFullyConnectedLayer(fullyConnectedDescriptor, node.name().c_str());</div> |
| <div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>  </div> |
| <div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>  {</div> |
| <div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>  }</div> |
| <div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>  </div> |
| <div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>  <span class="comment">// if transA != 0, add transpose to the input0</span></div> |
| <div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>  <span class="keywordflow">if</span> (transA != 0)</div> |
| <div class="line"><a name="l02092"></a><span class="lineno"> 2092</span>  {</div> |
| <div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>  std::string transAName = <span class="stringliteral">"transpose_"</span> + node.input(0);</div> |
| <div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>  <a class="code" href="structarmnn_1_1_transpose_descriptor.html">armnn::TransposeDescriptor</a> transposeADescriptor;</div> |
| <div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>  transposeADescriptor.<a class="code" href="structarmnn_1_1_transpose_descriptor.html#a14433af2b223695b40d8c8f8ba2ebb8f">m_DimMappings</a> = { 1, 0 };</div> |
| <div class="line"><a name="l02096"></a><span class="lineno"> 2096</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* transALayer = m_Network->AddTransposeLayer(transposeADescriptor, transAName.c_str());</div> |
| <div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>  </div> |
| <div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>  <span class="keywordflow">if</span> (!transALayer)</div> |
| <div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>  {</div> |
| <div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>  }</div> |
| <div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>  </div> |
| <div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>  <span class="keyword">auto</span> transAInfo = ComputeOutputInfo({ transAName }, transALayer, { input0Shape });</div> |
| <div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>  transALayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(transAInfo[0]);</div> |
| <div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>  transALayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0u));</div> |
| <div class="line"><a name="l02106"></a><span class="lineno"> 2106</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>  RegisterInputSlot(transALayer, node.input(0), 0);</div> |
| <div class="line"><a name="l02108"></a><span class="lineno"> 2108</span>  input0Shape = transAInfo[0].GetShape();</div> |
| <div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>  }</div> |
| <div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>  {</div> |
| <div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>  RegisterInputSlot(layer, node.input(0), 0);</div> |
| <div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>  }</div> |
| <div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>  </div> |
| <div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>  <span class="comment">// Add constant layer to store weights/biases and connect to FullyConnected layer.</span></div> |
| <div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>  <span class="keywordflow">if</span>(m_TensorsInfo[node.input(1)].isConstant())</div> |
| <div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>  {</div> |
| <div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* weightsLayer = m_Network->AddConstantLayer(CreateConstTensor(node.input(1)).first);</div> |
| <div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> weightInfo = *m_TensorsInfo[node.input(1)].m_info;</div> |
| <div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>  weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div> |
| <div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(weightInfo);</div> |
| <div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>  </div> |
| <div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>  <span class="comment">// if alpha != 1, multiply to the weight</span></div> |
| <div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>  <span class="keywordflow">if</span> (alpha != 1)</div> |
| <div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>  {</div> |
| <div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>  std::string activationName = <span class="stringliteral">"activation_"</span> + node.input(1);</div> |
| <div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.html">armnn::ActivationDescriptor</a> activationDescriptor;</div> |
| <div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a017b2990003a014234f13e999dc7c689">m_A</a> = alpha;</div> |
| <div class="line"><a name="l02129"></a><span class="lineno"> 2129</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.html#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::Linear;</div> |
| <div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* actLayer = m_Network->AddActivationLayer(activationDescriptor, activationName.c_str());</div> |
| <div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>  </div> |
| <div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>  <span class="keywordflow">if</span> (!actLayer)</div> |
| <div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  {</div> |
| <div class="line"><a name="l02134"></a><span class="lineno"> 2134</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02135"></a><span class="lineno"> 2135</span>  }</div> |
| <div class="line"><a name="l02136"></a><span class="lineno"> 2136</span>  </div> |
| <div class="line"><a name="l02137"></a><span class="lineno"> 2137</span>  <span class="keyword">auto</span> actInfo = ComputeOutputInfo({ activationName }, actLayer, { weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() });</div> |
| <div class="line"><a name="l02138"></a><span class="lineno"> 2138</span>  actLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(actInfo[0]);</div> |
| <div class="line"><a name="l02139"></a><span class="lineno"> 2139</span>  actLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1u));</div> |
| <div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(actLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0u));</div> |
| <div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>  input1Shape = actInfo[0].GetShape();</div> |
| <div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>  }</div> |
| <div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>  {</div> |
| <div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1u));</div> |
| <div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  input1Shape = weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div> |
| <div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>  }</div> |
| <div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>  }</div> |
| <div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>  {</div> |
| <div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>  <span class="comment">// if alpha != 1, multiply to the weight</span></div> |
| <div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>  <span class="keywordflow">if</span> (alpha != 1)</div> |
| <div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>  {</div> |
| <div class="line"><a name="l02154"></a><span class="lineno"> 2154</span>  std::string activationName = <span class="stringliteral">"activation_"</span> + node.input(1);</div> |
| <div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.html">armnn::ActivationDescriptor</a> activationDescriptor;</div> |
| <div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a017b2990003a014234f13e999dc7c689">m_A</a> = alpha;</div> |
| <div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.html#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::Linear;</div> |
| <div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* actLayer = m_Network->AddActivationLayer(activationDescriptor, activationName.c_str());</div> |
| <div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>  </div> |
| <div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>  <span class="keywordflow">if</span> (!actLayer)</div> |
| <div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>  {</div> |
| <div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>  }</div> |
| <div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>  </div> |
| <div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>  <span class="keyword">auto</span> actInfo = ComputeOutputInfo({ activationName }, actLayer, { input1Shape });</div> |
| <div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>  actLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(actInfo[0]);</div> |
| <div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>  actLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1u));</div> |
| <div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>  RegisterInputSlot(actLayer, node.input(1), 0);</div> |
| <div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>  input1Shape = actInfo[0].GetShape();</div> |
| <div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>  }</div> |
| <div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>  {</div> |
| <div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>  RegisterInputSlot(layer, node.input(1), 1);</div> |
| <div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>  }</div> |
| <div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>  }</div> |
| <div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>  </div> |
| <div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>  <span class="keywordflow">if</span>(biasEnabled && m_TensorsInfo[node.input(2)].isConstant())</div> |
| <div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>  {</div> |
| <div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>  To1DTensor(node.input(2), <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div> |
| <div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* biasLayer = m_Network->AddConstantLayer(CreateConstTensor(node.input(2)).first);</div> |
| <div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> biasInfo = *m_TensorsInfo[node.input(2)].m_info;</div> |
| <div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>  biasInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div> |
| <div class="line"><a name="l02183"></a><span class="lineno"> 2183</span>  biasLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(biasInfo);</div> |
| <div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>  </div> |
| <div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>  <span class="comment">// if beta != 1, multiply to the bias</span></div> |
| <div class="line"><a name="l02186"></a><span class="lineno"> 2186</span>  <span class="keywordflow">if</span> (beta != 1)</div> |
| <div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>  {</div> |
| <div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>  std::string activationName = <span class="stringliteral">"activation_"</span> + node.input(2);</div> |
| <div class="line"><a name="l02189"></a><span class="lineno"> 2189</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.html">armnn::ActivationDescriptor</a> activationDescriptor;</div> |
| <div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a017b2990003a014234f13e999dc7c689">m_A</a> = beta;</div> |
| <div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.html#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::Linear;</div> |
| <div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* actLayer = m_Network->AddActivationLayer(activationDescriptor, activationName.c_str());</div> |
| <div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>  </div> |
| <div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>  <span class="keywordflow">if</span> (!actLayer)</div> |
| <div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>  {</div> |
| <div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>  }</div> |
| <div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>  </div> |
| <div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>  <span class="keyword">auto</span> actInfo = ComputeOutputInfo({ activationName }, actLayer, { biasInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() });</div> |
| <div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>  actLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(actInfo[0]);</div> |
| <div class="line"><a name="l02201"></a><span class="lineno"> 2201</span>  actLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2u));</div> |
| <div class="line"><a name="l02202"></a><span class="lineno"> 2202</span>  biasLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(actLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0u));</div> |
| <div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>  }</div> |
| <div class="line"><a name="l02204"></a><span class="lineno"> 2204</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l02205"></a><span class="lineno"> 2205</span>  {</div> |
| <div class="line"><a name="l02206"></a><span class="lineno"> 2206</span>  biasLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2u));</div> |
| <div class="line"><a name="l02207"></a><span class="lineno"> 2207</span>  }</div> |
| <div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>  }</div> |
| <div class="line"><a name="l02209"></a><span class="lineno"> 2209</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (biasEnabled)</div> |
| <div class="line"><a name="l02210"></a><span class="lineno"> 2210</span>  {</div> |
| <div class="line"><a name="l02211"></a><span class="lineno"> 2211</span>  <span class="comment">// Currently we support non-constant tensor of input C (bias) of Gemm when the dimension is 1</span></div> |
| <div class="line"><a name="l02212"></a><span class="lineno"> 2212</span>  <span class="keywordflow">if</span> (m_TensorsInfo[node.input(2)].m_info->GetNumDimensions() != 1)</div> |
| <div class="line"><a name="l02213"></a><span class="lineno"> 2213</span>  {</div> |
| <div class="line"><a name="l02214"></a><span class="lineno"> 2214</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"The parser supports constant or non-constant with 1 dimension for "</span></div> |
| <div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>  <span class="stringliteral">"Input C of Gemm. Input '{}' in '{}' is not supported '{}'"</span>,</div> |
| <div class="line"><a name="l02216"></a><span class="lineno"> 2216</span>  node.input(2),</div> |
| <div class="line"><a name="l02217"></a><span class="lineno"> 2217</span>  node.name(),</div> |
| <div class="line"><a name="l02218"></a><span class="lineno"> 2218</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02219"></a><span class="lineno"> 2219</span>  }</div> |
| <div class="line"><a name="l02220"></a><span class="lineno"> 2220</span>  <span class="comment">// if beta != 1, multiply to the bias</span></div> |
| <div class="line"><a name="l02221"></a><span class="lineno"> 2221</span>  <span class="keywordflow">if</span> (beta != 1)</div> |
| <div class="line"><a name="l02222"></a><span class="lineno"> 2222</span>  {</div> |
| <div class="line"><a name="l02223"></a><span class="lineno"> 2223</span>  std::string activationName = <span class="stringliteral">"activation_"</span> + node.input(2);</div> |
| <div class="line"><a name="l02224"></a><span class="lineno"> 2224</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.html">armnn::ActivationDescriptor</a> activationDescriptor;</div> |
| <div class="line"><a name="l02225"></a><span class="lineno"> 2225</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a017b2990003a014234f13e999dc7c689">m_A</a> = beta;</div> |
| <div class="line"><a name="l02226"></a><span class="lineno"> 2226</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.html#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::Linear;</div> |
| <div class="line"><a name="l02227"></a><span class="lineno"> 2227</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* actLayer = m_Network->AddActivationLayer(activationDescriptor, activationName.c_str());</div> |
| <div class="line"><a name="l02228"></a><span class="lineno"> 2228</span>  </div> |
| <div class="line"><a name="l02229"></a><span class="lineno"> 2229</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>  {</div> |
| <div class="line"><a name="l02231"></a><span class="lineno"> 2231</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02232"></a><span class="lineno"> 2232</span>  }</div> |
| <div class="line"><a name="l02233"></a><span class="lineno"> 2233</span>  </div> |
| <div class="line"><a name="l02234"></a><span class="lineno"> 2234</span>  <span class="keyword">auto</span> actInfo = ComputeOutputInfo({ activationName },</div> |
| <div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>  actLayer,</div> |
| <div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>  { m_TensorsInfo[node.input(2)].m_info->GetShape() });</div> |
| <div class="line"><a name="l02237"></a><span class="lineno"> 2237</span>  actLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(actInfo[0]);</div> |
| <div class="line"><a name="l02238"></a><span class="lineno"> 2238</span>  actLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2u));</div> |
| <div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>  RegisterInputSlot(actLayer, node.input(2), 0);</div> |
| <div class="line"><a name="l02240"></a><span class="lineno"> 2240</span>  }</div> |
| <div class="line"><a name="l02241"></a><span class="lineno"> 2241</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l02242"></a><span class="lineno"> 2242</span>  {</div> |
| <div class="line"><a name="l02243"></a><span class="lineno"> 2243</span>  RegisterInputSlot(layer, node.input(2), 2);</div> |
| <div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>  }</div> |
| <div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>  }</div> |
| <div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>  </div> |
| <div class="line"><a name="l02247"></a><span class="lineno"> 2247</span>  <span class="comment">// Set final output of the FullyConnected layer</span></div> |
| <div class="line"><a name="l02248"></a><span class="lineno"> 2248</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({ node.output(0) }, layer,</div> |
| <div class="line"><a name="l02249"></a><span class="lineno"> 2249</span>  { input0Shape, input1Shape });</div> |
| <div class="line"><a name="l02250"></a><span class="lineno"> 2250</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l02251"></a><span class="lineno"> 2251</span>  </div> |
| <div class="line"><a name="l02252"></a><span class="lineno"> 2252</span>  RegisterOutputSlots(layer, {node.output(0)});</div> |
| <div class="line"><a name="l02253"></a><span class="lineno"> 2253</span> }</div> |
| <div class="line"><a name="l02254"></a><span class="lineno"> 2254</span>  </div> |
| <div class="line"><a name="l02255"></a><span class="lineno"> 2255</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseGlobalAveragePool(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l02256"></a><span class="lineno"> 2256</span> {</div> |
| <div class="line"><a name="l02257"></a><span class="lineno"> 2257</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">Pooling2dDescriptor</a> desc = <a class="code" href="namespacearmnn_deserializer.html#a7e75f47f676327bce37149932aa4a011">Pooling2dDescriptor</a>();</div> |
| <div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = PoolingAlgorithm::Average;</div> |
| <div class="line"><a name="l02259"></a><span class="lineno"> 2259</span>  </div> |
| <div class="line"><a name="l02260"></a><span class="lineno"> 2260</span>  <span class="comment">//kernel size is the same as input</span></div> |
| <div class="line"><a name="l02261"></a><span class="lineno"> 2261</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape();</div> |
| <div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = inputShape[3];</div> |
| <div class="line"><a name="l02263"></a><span class="lineno"> 2263</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = inputShape[2];</div> |
| <div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>  </div> |
| <div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = m_Network->AddPooling2dLayer(desc, node.name().c_str());</div> |
| <div class="line"><a name="l02266"></a><span class="lineno"> 2266</span>  </div> |
| <div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l02268"></a><span class="lineno"> 2268</span>  {</div> |
| <div class="line"><a name="l02269"></a><span class="lineno"> 2269</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02270"></a><span class="lineno"> 2270</span>  }</div> |
| <div class="line"><a name="l02271"></a><span class="lineno"> 2271</span>  </div> |
| <div class="line"><a name="l02272"></a><span class="lineno"> 2272</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({node.output(0)}, layer, {inputShape});</div> |
| <div class="line"><a name="l02273"></a><span class="lineno"> 2273</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l02274"></a><span class="lineno"> 2274</span>  </div> |
| <div class="line"><a name="l02275"></a><span class="lineno"> 2275</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div> |
| <div class="line"><a name="l02277"></a><span class="lineno"> 2277</span>  RegisterInputSlots(layer, {node.input(0)});</div> |
| <div class="line"><a name="l02278"></a><span class="lineno"> 2278</span>  </div> |
| <div class="line"><a name="l02279"></a><span class="lineno"> 2279</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l02280"></a><span class="lineno"> 2280</span>  RegisterOutputSlots(layer, {node.output(0)});</div> |
| <div class="line"><a name="l02281"></a><span class="lineno"> 2281</span> }</div> |
| <div class="line"><a name="l02282"></a><span class="lineno"> 2282</span>  </div> |
| <div class="line"><a name="l02283"></a><span class="lineno"> 2283</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseMaxPool(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l02284"></a><span class="lineno"> 2284</span> {</div> |
| <div class="line"><a name="l02285"></a><span class="lineno"> 2285</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">Pooling2dDescriptor</a> desc;</div> |
| <div class="line"><a name="l02286"></a><span class="lineno"> 2286</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = PoolingAlgorithm::Max;</div> |
| <div class="line"><a name="l02287"></a><span class="lineno"> 2287</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = PaddingMethod::Exclude;</div> |
| <div class="line"><a name="l02288"></a><span class="lineno"> 2288</span>  AddPoolingLayer(node, desc);</div> |
| <div class="line"><a name="l02289"></a><span class="lineno"> 2289</span> }</div> |
| <div class="line"><a name="l02290"></a><span class="lineno"> 2290</span>  </div> |
| <div class="line"><a name="l02291"></a><span class="lineno"> 2291</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseShape(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l02292"></a><span class="lineno"> 2292</span> {</div> |
| <div class="line"><a name="l02293"></a><span class="lineno"> 2293</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.input_size()), 1);</div> |
| <div class="line"><a name="l02294"></a><span class="lineno"> 2294</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.output_size()), 1);</div> |
| <div class="line"><a name="l02295"></a><span class="lineno"> 2295</span>  </div> |
| <div class="line"><a name="l02296"></a><span class="lineno"> 2296</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = m_Network->AddShapeLayer(node.name().c_str());</div> |
| <div class="line"><a name="l02297"></a><span class="lineno"> 2297</span>  </div> |
| <div class="line"><a name="l02298"></a><span class="lineno"> 2298</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l02299"></a><span class="lineno"> 2299</span>  {</div> |
| <div class="line"><a name="l02300"></a><span class="lineno"> 2300</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02301"></a><span class="lineno"> 2301</span>  }</div> |
| <div class="line"><a name="l02302"></a><span class="lineno"> 2302</span>  </div> |
| <div class="line"><a name="l02303"></a><span class="lineno"> 2303</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape();</div> |
| <div class="line"><a name="l02304"></a><span class="lineno"> 2304</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({node.output(0)}, layer, {inputShape}, onnx::TensorProto::INT64);</div> |
| <div class="line"><a name="l02305"></a><span class="lineno"> 2305</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div> |
| <div class="line"><a name="l02306"></a><span class="lineno"> 2306</span>  </div> |
| <div class="line"><a name="l02307"></a><span class="lineno"> 2307</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l02308"></a><span class="lineno"> 2308</span>  RegisterInputSlots(layer, {node.input(0)});</div> |
| <div class="line"><a name="l02309"></a><span class="lineno"> 2309</span>  </div> |
| <div class="line"><a name="l02310"></a><span class="lineno"> 2310</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div> |
| <div class="line"><a name="l02311"></a><span class="lineno"> 2311</span>  RegisterOutputSlots(layer, {node.output(0)});</div> |
| <div class="line"><a name="l02312"></a><span class="lineno"> 2312</span> }</div> |
| <div class="line"><a name="l02313"></a><span class="lineno"> 2313</span>  </div> |
| <div class="line"><a name="l02314"></a><span class="lineno"> 2314</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseReshape(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l02315"></a><span class="lineno"> 2315</span> {</div> |
| <div class="line"><a name="l02316"></a><span class="lineno"> 2316</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.input_size()), 2);</div> |
| <div class="line"><a name="l02317"></a><span class="lineno"> 2317</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(node.output_size()), 1);</div> |
| <div class="line"><a name="l02318"></a><span class="lineno"> 2318</span>  </div> |
| <div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>  <a class="code" href="_onnx_parser_8cpp.html#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a>(node.name(), node.input(0),</div> |
| <div class="line"><a name="l02320"></a><span class="lineno"> 2320</span>  m_TensorsInfo[node.input(0)].m_dtype,</div> |
| <div class="line"><a name="l02321"></a><span class="lineno"> 2321</span>  onnx::TensorProto::FLOAT); <span class="comment">//input</span></div> |
| <div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>  <a class="code" href="_onnx_parser_8cpp.html#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a>(node.name(), node.input(1),</div> |
| <div class="line"><a name="l02323"></a><span class="lineno"> 2323</span>  m_TensorsInfo[node.input(1)].m_dtype,</div> |
| <div class="line"><a name="l02324"></a><span class="lineno"> 2324</span>  onnx::TensorProto::INT64); <span class="comment">//shape</span></div> |
| <div class="line"><a name="l02325"></a><span class="lineno"> 2325</span>  </div> |
| <div class="line"><a name="l02326"></a><span class="lineno"> 2326</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape();</div> |
| <div class="line"><a name="l02327"></a><span class="lineno"> 2327</span>  </div> |
| <div class="line"><a name="l02328"></a><span class="lineno"> 2328</span>  std::vector<unsigned int> targetShape;</div> |
| <div class="line"><a name="l02329"></a><span class="lineno"> 2329</span>  <span class="keywordflow">if</span>(m_TensorsInfo[node.input(1)].isConstant())</div> |
| <div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>  {</div> |
| <div class="line"><a name="l02331"></a><span class="lineno"> 2331</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dims = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(m_TensorsInfo[node.input(1)].m_tensor->int64_data_size());</div> |
| <div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>  targetShape.reserve(dims);</div> |
| <div class="line"><a name="l02333"></a><span class="lineno"> 2333</span>  </div> |
| <div class="line"><a name="l02334"></a><span class="lineno"> 2334</span>  <span class="keywordflow">for</span>(uint i = 0; i < dims; i++)</div> |
| <div class="line"><a name="l02335"></a><span class="lineno"> 2335</span>  {</div> |
| <div class="line"><a name="l02336"></a><span class="lineno"> 2336</span>  <span class="keywordtype">int</span> val = <a class="code" href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(m_TensorsInfo[node.input(1)].m_tensor->int64_data(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(i)));</div> |
| <div class="line"><a name="l02337"></a><span class="lineno"> 2337</span>  targetShape[i]= <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(val);</div> |
| <div class="line"><a name="l02338"></a><span class="lineno"> 2338</span>  }</div> |
| <div class="line"><a name="l02339"></a><span class="lineno"> 2339</span>  }</div> |
| <div class="line"><a name="l02340"></a><span class="lineno"> 2340</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l02341"></a><span class="lineno"> 2341</span>  {</div> |
| <div class="line"><a name="l02342"></a><span class="lineno"> 2342</span>  <span class="comment">// The parser only supports shape (batch, -1) or (-1) for non-constant shape input.</span></div> |
| <div class="line"><a name="l02343"></a><span class="lineno"> 2343</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dims = m_TensorsInfo[node.input(1)].m_info->GetNumDimensions();</div> |
| <div class="line"><a name="l02344"></a><span class="lineno"> 2344</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> shapes = m_TensorsInfo[node.input(1)].m_info->GetShape();</div> |
| <div class="line"><a name="l02345"></a><span class="lineno"> 2345</span>  <span class="keywordflow">if</span> (dims != 1 || shapes[0] > 2)</div> |
| <div class="line"><a name="l02346"></a><span class="lineno"> 2346</span>  {</div> |
| <div class="line"><a name="l02347"></a><span class="lineno"> 2347</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Invalid input shape '{}' in Reshape layer '{}' {}"</span>,</div> |
| <div class="line"><a name="l02348"></a><span class="lineno"> 2348</span>  node.input(1),</div> |
| <div class="line"><a name="l02349"></a><span class="lineno"> 2349</span>  node.name(),</div> |
| <div class="line"><a name="l02350"></a><span class="lineno"> 2350</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02351"></a><span class="lineno"> 2351</span>  }</div> |
| <div class="line"><a name="l02352"></a><span class="lineno"> 2352</span>  </div> |
| <div class="line"><a name="l02353"></a><span class="lineno"> 2353</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputElements = m_TensorsInfo[node.input(0)].m_info->GetNumElements();</div> |
| <div class="line"><a name="l02354"></a><span class="lineno"> 2354</span>  <span class="keywordflow">if</span> (shapes[0] == 1)</div> |
| <div class="line"><a name="l02355"></a><span class="lineno"> 2355</span>  {</div> |
| <div class="line"><a name="l02356"></a><span class="lineno"> 2356</span>  targetShape = { numInputElements };</div> |
| <div class="line"><a name="l02357"></a><span class="lineno"> 2357</span>  }</div> |
| <div class="line"><a name="l02358"></a><span class="lineno"> 2358</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (shapes[0] == 2)</div> |
| <div class="line"><a name="l02359"></a><span class="lineno"> 2359</span>  {</div> |
| <div class="line"><a name="l02360"></a><span class="lineno"> 2360</span>  targetShape = { inputShape[0] , numInputElements / inputShape[0] };</div> |
| <div class="line"><a name="l02361"></a><span class="lineno"> 2361</span>  }</div> |
| <div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>  }</div> |
| <div class="line"><a name="l02363"></a><span class="lineno"> 2363</span>  </div> |
| <div class="line"><a name="l02364"></a><span class="lineno"> 2364</span>  <span class="keywordflow">if</span>(m_TensorsInfo[node.input(0)].isConstant())</div> |
| <div class="line"><a name="l02365"></a><span class="lineno"> 2365</span>  {</div> |
| <div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>  <span class="comment">//make a new cst tensor -> move the data to the output tensor (the shape is already good in the output tensor)</span></div> |
| <div class="line"><a name="l02367"></a><span class="lineno"> 2367</span>  <span class="keywordflow">if</span>(m_TensorsInfo.count(node.output(0)) == 0)</div> |
| <div class="line"><a name="l02368"></a><span class="lineno"> 2368</span>  {</div> |
| <div class="line"><a name="l02369"></a><span class="lineno"> 2369</span>  m_TensorsInfo[node.output(0)] = OnnxTensor();</div> |
| <div class="line"><a name="l02370"></a><span class="lineno"> 2370</span>  }</div> |
| <div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>  m_TensorsInfo[node.output(0)].m_tensor =</div> |
| <div class="line"><a name="l02372"></a><span class="lineno"> 2372</span>  std::make_unique<onnx::TensorProto>(*m_TensorsInfo[node.input(0)].m_tensor);</div> |
| <div class="line"><a name="l02373"></a><span class="lineno"> 2373</span>  }</div> |
| <div class="line"><a name="l02374"></a><span class="lineno"> 2374</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l02375"></a><span class="lineno"> 2375</span>  {</div> |
| <div class="line"><a name="l02376"></a><span class="lineno"> 2376</span>  <span class="keywordflow">if</span>(m_TensorsInfo.count(node.output(0)) == 0 || m_TensorsInfo[node.output(0)].m_info == <span class="keyword">nullptr</span>)</div> |
| <div class="line"><a name="l02377"></a><span class="lineno"> 2377</span>  {</div> |
| <div class="line"><a name="l02378"></a><span class="lineno"> 2378</span>  <span class="keyword">auto</span> outInfo = ComputeReshapeInfo(</div> |
| <div class="line"><a name="l02379"></a><span class="lineno"> 2379</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>(<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(targetShape.size()), targetShape.data()),</div> |
| <div class="line"><a name="l02380"></a><span class="lineno"> 2380</span>  inputShape, node.output(0));</div> |
| <div class="line"><a name="l02381"></a><span class="lineno"> 2381</span>  m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(outInfo);</div> |
| <div class="line"><a name="l02382"></a><span class="lineno"> 2382</span>  }</div> |
| <div class="line"><a name="l02383"></a><span class="lineno"> 2383</span>  </div> |
| <div class="line"><a name="l02384"></a><span class="lineno"> 2384</span>  CreateReshapeLayer(node.input(0), node.output(0), node.name());</div> |
| <div class="line"><a name="l02385"></a><span class="lineno"> 2385</span>  }</div> |
| <div class="line"><a name="l02386"></a><span class="lineno"> 2386</span> }</div> |
| <div class="line"><a name="l02387"></a><span class="lineno"> 2387</span>  </div> |
| <div class="line"><a name="l02388"></a><span class="lineno"> 2388</span> <span class="keywordtype">void</span> OnnxParserImpl::ParseUnsqueeze(<span class="keyword">const</span> onnx::NodeProto& node)</div> |
| <div class="line"><a name="l02389"></a><span class="lineno"> 2389</span> {</div> |
| <div class="line"><a name="l02390"></a><span class="lineno"> 2390</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(armnn::numeric_cast<size_t>(node.input_size()), 1, 2);</div> |
| <div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>  <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(armnn::numeric_cast<size_t>(node.output_size()), 1);</div> |
| <div class="line"><a name="l02392"></a><span class="lineno"> 2392</span>  </div> |
| <div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape();</div> |
| <div class="line"><a name="l02394"></a><span class="lineno"> 2394</span>  std::vector<uint32_t> dims;</div> |
| <div class="line"><a name="l02395"></a><span class="lineno"> 2395</span>  <span class="keywordflow">if</span> (node.input_size() == 1 && node.attribute_size() > 0)</div> |
| <div class="line"><a name="l02396"></a><span class="lineno"> 2396</span>  {</div> |
| <div class="line"><a name="l02397"></a><span class="lineno"> 2397</span>  dims = ReadMandatoryNodeUint32ListAttribute(node, <span class="stringliteral">"axes"</span>);</div> |
| <div class="line"><a name="l02398"></a><span class="lineno"> 2398</span>  }</div> |
| <div class="line"><a name="l02399"></a><span class="lineno"> 2399</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l02400"></a><span class="lineno"> 2400</span>  {</div> |
| <div class="line"><a name="l02401"></a><span class="lineno"> 2401</span>  <a class="code" href="_onnx_parser_8cpp.html#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a>(node.name(), node.input(1),</div> |
| <div class="line"><a name="l02402"></a><span class="lineno"> 2402</span>  m_TensorsInfo[node.input(1)].m_dtype,</div> |
| <div class="line"><a name="l02403"></a><span class="lineno"> 2403</span>  onnx::TensorProto::INT64); <span class="comment">//axes</span></div> |
| <div class="line"><a name="l02404"></a><span class="lineno"> 2404</span>  </div> |
| <div class="line"><a name="l02405"></a><span class="lineno"> 2405</span>  <span class="keyword">auto</span> int64Axes = m_TensorsInfo[node.input(1)].m_tensor->int64_data().data();</div> |
| <div class="line"><a name="l02406"></a><span class="lineno"> 2406</span>  uint numDim = armnn::numeric_cast<uint>(m_TensorsInfo[node.input(1)].m_tensor->int64_data_size());</div> |
| <div class="line"><a name="l02407"></a><span class="lineno"> 2407</span>  </div> |
| <div class="line"><a name="l02408"></a><span class="lineno"> 2408</span>  <span class="keywordflow">for</span>(uint i = 0; i < numDim; i++)</div> |
| <div class="line"><a name="l02409"></a><span class="lineno"> 2409</span>  {</div> |
| <div class="line"><a name="l02410"></a><span class="lineno"> 2410</span>  uint32_t uint32Value = <a class="code" href="_verification_helpers_8hpp.html#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(int64Axes[i]));</div> |
| <div class="line"><a name="l02411"></a><span class="lineno"> 2411</span>  dims.push_back(uint32Value);</div> |
| <div class="line"><a name="l02412"></a><span class="lineno"> 2412</span>  }</div> |
| <div class="line"><a name="l02413"></a><span class="lineno"> 2413</span>  }</div> |
| <div class="line"><a name="l02414"></a><span class="lineno"> 2414</span>  </div> |
| <div class="line"><a name="l02415"></a><span class="lineno"> 2415</span>  <span class="comment">// Ensure that the axes are sorted</span></div> |
| <div class="line"><a name="l02416"></a><span class="lineno"> 2416</span>  std::sort(dims.begin(), dims.end());</div> |
| <div class="line"><a name="l02417"></a><span class="lineno"> 2417</span>  </div> |
| <div class="line"><a name="l02418"></a><span class="lineno"> 2418</span>  std::vector<unsigned int> targetShape;</div> |
| <div class="line"><a name="l02419"></a><span class="lineno"> 2419</span>  </div> |
| <div class="line"><a name="l02420"></a><span class="lineno"> 2420</span>  <span class="keywordflow">if</span> (inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a5a212540c00931bd2a4b4041beda33ae">GetDimensionality</a>() != Dimensionality::Scalar)</div> |
| <div class="line"><a name="l02421"></a><span class="lineno"> 2421</span>  {</div> |
| <div class="line"><a name="l02422"></a><span class="lineno"> 2422</span>  <span class="keywordflow">for</span>(uint i = 0; i < inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</div> |
| <div class="line"><a name="l02423"></a><span class="lineno"> 2423</span>  {</div> |
| <div class="line"><a name="l02424"></a><span class="lineno"> 2424</span>  targetShape.push_back(inputShape[i]);</div> |
| <div class="line"><a name="l02425"></a><span class="lineno"> 2425</span>  }</div> |
| <div class="line"><a name="l02426"></a><span class="lineno"> 2426</span>  }</div> |
| <div class="line"><a name="l02427"></a><span class="lineno"> 2427</span>  </div> |
| <div class="line"><a name="l02428"></a><span class="lineno"> 2428</span>  <span class="keywordflow">for</span>(uint i = 0; i < dims.size(); i++)</div> |
| <div class="line"><a name="l02429"></a><span class="lineno"> 2429</span>  {</div> |
| <div class="line"><a name="l02430"></a><span class="lineno"> 2430</span>  targetShape.insert(targetShape.begin() + armnn::numeric_cast<int>(dims[i]), 1);</div> |
| <div class="line"><a name="l02431"></a><span class="lineno"> 2431</span>  }</div> |
| <div class="line"><a name="l02432"></a><span class="lineno"> 2432</span>  </div> |
| <div class="line"><a name="l02433"></a><span class="lineno"> 2433</span>  <span class="keyword">auto</span> outInfo = ComputeReshapeInfo(<a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>(<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(targetShape.size()), targetShape.data()),</div> |
| <div class="line"><a name="l02434"></a><span class="lineno"> 2434</span>  inputShape, node.output(0), m_TensorsInfo[node.input(0)].m_info->GetDataType());</div> |
| <div class="line"><a name="l02435"></a><span class="lineno"> 2435</span>  m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(outInfo);</div> |
| <div class="line"><a name="l02436"></a><span class="lineno"> 2436</span>  m_TensorsInfo[node.output(0)].m_dtype = m_TensorsInfo[node.input(0)].m_dtype;</div> |
| <div class="line"><a name="l02437"></a><span class="lineno"> 2437</span>  </div> |
| <div class="line"><a name="l02438"></a><span class="lineno"> 2438</span>  CreateReshapeLayer(node.input(0), node.output(0), node.name());</div> |
| <div class="line"><a name="l02439"></a><span class="lineno"> 2439</span> }</div> |
| <div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>  </div> |
| <div class="line"><a name="l02441"></a><span class="lineno"> 2441</span> <span class="keywordtype">void</span> OnnxParserImpl::PrependForBroadcast(<span class="keyword">const</span> std::string& outputName,</div> |
| <div class="line"><a name="l02442"></a><span class="lineno"> 2442</span>  <span class="keyword">const</span> std::string& input0,</div> |
| <div class="line"><a name="l02443"></a><span class="lineno"> 2443</span>  <span class="keyword">const</span> std::string& input1)</div> |
| <div class="line"><a name="l02444"></a><span class="lineno"> 2444</span> {</div> |
| <div class="line"><a name="l02445"></a><span class="lineno"> 2445</span>  <span class="comment">//input0 should be reshaped to have same number of dim as input1</span></div> |
| <div class="line"><a name="l02446"></a><span class="lineno"> 2446</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>(*m_TensorsInfo[input0].m_info);</div> |
| <div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>  </div> |
| <div class="line"><a name="l02448"></a><span class="lineno"> 2448</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> input0Shape = m_TensorsInfo[input0].m_info->GetShape();</div> |
| <div class="line"><a name="l02449"></a><span class="lineno"> 2449</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> input1Shape = m_TensorsInfo[input1].m_info->GetShape();</div> |
| <div class="line"><a name="l02450"></a><span class="lineno"> 2450</span>  </div> |
| <div class="line"><a name="l02451"></a><span class="lineno"> 2451</span>  uint32_t diff = input1Shape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div> |
| <div class="line"><a name="l02452"></a><span class="lineno"> 2452</span>  std::vector<uint32_t> newShape;</div> |
| <div class="line"><a name="l02453"></a><span class="lineno"> 2453</span>  <span class="keywordflow">while</span>(diff > 0)</div> |
| <div class="line"><a name="l02454"></a><span class="lineno"> 2454</span>  {</div> |
| <div class="line"><a name="l02455"></a><span class="lineno"> 2455</span>  newShape.push_back(1);</div> |
| <div class="line"><a name="l02456"></a><span class="lineno"> 2456</span>  diff--;</div> |
| <div class="line"><a name="l02457"></a><span class="lineno"> 2457</span>  }</div> |
| <div class="line"><a name="l02458"></a><span class="lineno"> 2458</span>  <span class="keywordflow">for</span> (uint dim = 0; dim < input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++dim)</div> |
| <div class="line"><a name="l02459"></a><span class="lineno"> 2459</span>  {</div> |
| <div class="line"><a name="l02460"></a><span class="lineno"> 2460</span>  newShape.push_back(input0Shape[dim]);</div> |
| <div class="line"><a name="l02461"></a><span class="lineno"> 2461</span>  }</div> |
| <div class="line"><a name="l02462"></a><span class="lineno"> 2462</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>(<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(newShape.size()), newShape.data()));</div> |
| <div class="line"><a name="l02463"></a><span class="lineno"> 2463</span>  </div> |
| <div class="line"><a name="l02464"></a><span class="lineno"> 2464</span>  <span class="comment">//add the new tensor to m_TensorsInfo</span></div> |
| <div class="line"><a name="l02465"></a><span class="lineno"> 2465</span>  m_TensorsInfo[outputName] = OnnxTensor();</div> |
| <div class="line"><a name="l02466"></a><span class="lineno"> 2466</span>  m_TensorsInfo[outputName].m_info = std::make_unique<TensorInfo>(outputTensorInfo);</div> |
| <div class="line"><a name="l02467"></a><span class="lineno"> 2467</span>  </div> |
| <div class="line"><a name="l02468"></a><span class="lineno"> 2468</span>  <span class="comment">//add reshape layer if the parent was not constant...</span></div> |
| <div class="line"><a name="l02469"></a><span class="lineno"> 2469</span>  <span class="keywordflow">if</span>( ! m_TensorsInfo[input0].isConstant())</div> |
| <div class="line"><a name="l02470"></a><span class="lineno"> 2470</span>  {</div> |
| <div class="line"><a name="l02471"></a><span class="lineno"> 2471</span>  CreateReshapeLayer(input0, outputName, fmt::format(<span class="stringliteral">"Add:reshapeOf{}"</span>, input0));</div> |
| <div class="line"><a name="l02472"></a><span class="lineno"> 2472</span>  }</div> |
| <div class="line"><a name="l02473"></a><span class="lineno"> 2473</span>  <span class="keywordflow">else</span> <span class="comment">//make it constant and it will be create in Add</span></div> |
| <div class="line"><a name="l02474"></a><span class="lineno"> 2474</span>  {</div> |
| <div class="line"><a name="l02475"></a><span class="lineno"> 2475</span>  m_TensorsInfo[outputName].m_tensor = std::make_unique<onnx::TensorProto>(*m_TensorsInfo[input0].m_tensor);</div> |
| <div class="line"><a name="l02476"></a><span class="lineno"> 2476</span>  </div> |
| <div class="line"><a name="l02477"></a><span class="lineno"> 2477</span>  }</div> |
| <div class="line"><a name="l02478"></a><span class="lineno"> 2478</span> }</div> |
| <div class="line"><a name="l02479"></a><span class="lineno"> 2479</span>  </div> |
| <div class="line"><a name="l02480"></a><span class="lineno"> 2480</span> <span class="keywordtype">void</span> OnnxParserImpl::SetupInputLayers()</div> |
| <div class="line"><a name="l02481"></a><span class="lineno"> 2481</span> {</div> |
| <div class="line"><a name="l02482"></a><span class="lineno"> 2482</span>  <span class="comment">//Find user input and add their layers</span></div> |
| <div class="line"><a name="l02483"></a><span class="lineno"> 2483</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> inputIndex = 0; inputIndex < m_Graph->input_size(); ++inputIndex)</div> |
| <div class="line"><a name="l02484"></a><span class="lineno"> 2484</span>  {</div> |
| <div class="line"><a name="l02485"></a><span class="lineno"> 2485</span>  <span class="keyword">auto</span> input = m_Graph->input(inputIndex);</div> |
| <div class="line"><a name="l02486"></a><span class="lineno"> 2486</span>  <span class="keywordflow">if</span> (!m_TensorsInfo[input.name()].isConstant())</div> |
| <div class="line"><a name="l02487"></a><span class="lineno"> 2487</span>  {</div> |
| <div class="line"><a name="l02488"></a><span class="lineno"> 2488</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer =</div> |
| <div class="line"><a name="l02489"></a><span class="lineno"> 2489</span>  m_Network->AddInputLayer(<span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.html#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a><span class="keyword">></span>(inputIndex), input.name().c_str());</div> |
| <div class="line"><a name="l02490"></a><span class="lineno"> 2490</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> tensorInfo = *m_TensorsInfo[input.name()].m_info;</div> |
| <div class="line"><a name="l02491"></a><span class="lineno"> 2491</span>  <span class="keywordflow">if</span> (tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>().<a class="code" href="classarmnn_1_1_tensor_shape.html#a5a212540c00931bd2a4b4041beda33ae">GetDimensionality</a>() == Dimensionality::NotSpecified)</div> |
| <div class="line"><a name="l02492"></a><span class="lineno"> 2492</span>  {</div> |
| <div class="line"><a name="l02493"></a><span class="lineno"> 2493</span>  <span class="keywordflow">if</span> (m_InputShapes.find(input.name()) == m_InputShapes.end())</div> |
| <div class="line"><a name="l02494"></a><span class="lineno"> 2494</span>  {</div> |
| <div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"The parser does not support dynamic tensor, "</span></div> |
| <div class="line"><a name="l02496"></a><span class="lineno"> 2496</span>  <span class="stringliteral">"please specify input shape for {}. {}"</span>,</div> |
| <div class="line"><a name="l02497"></a><span class="lineno"> 2497</span>  input.name(),</div> |
| <div class="line"><a name="l02498"></a><span class="lineno"> 2498</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02499"></a><span class="lineno"> 2499</span>  }</div> |
| <div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>  <span class="keywordflow">else</span></div> |
| <div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>  {</div> |
| <div class="line"><a name="l02502"></a><span class="lineno"> 2502</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(m_InputShapes[input.name()]);</div> |
| <div class="line"><a name="l02503"></a><span class="lineno"> 2503</span>  m_TensorsInfo[input.name()].m_info = std::make_unique<TensorInfo>(tensorInfo);</div> |
| <div class="line"><a name="l02504"></a><span class="lineno"> 2504</span>  }</div> |
| <div class="line"><a name="l02505"></a><span class="lineno"> 2505</span>  </div> |
| <div class="line"><a name="l02506"></a><span class="lineno"> 2506</span>  }</div> |
| <div class="line"><a name="l02507"></a><span class="lineno"> 2507</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div> |
| <div class="line"><a name="l02508"></a><span class="lineno"> 2508</span>  </div> |
| <div class="line"><a name="l02509"></a><span class="lineno"> 2509</span>  m_InputInfos[input.name()] = tensorInfo;</div> |
| <div class="line"><a name="l02510"></a><span class="lineno"> 2510</span>  </div> |
| <div class="line"><a name="l02511"></a><span class="lineno"> 2511</span>  RegisterOutputSlots(layer,{ input.name() });</div> |
| <div class="line"><a name="l02512"></a><span class="lineno"> 2512</span>  }</div> |
| <div class="line"><a name="l02513"></a><span class="lineno"> 2513</span>  }</div> |
| <div class="line"><a name="l02514"></a><span class="lineno"> 2514</span> }</div> |
| <div class="line"><a name="l02515"></a><span class="lineno"> 2515</span>  </div> |
| <div class="line"><a name="l02516"></a><span class="lineno"> 2516</span> <span class="keywordtype">void</span> OnnxParserImpl::SetupOutputLayers()</div> |
| <div class="line"><a name="l02517"></a><span class="lineno"> 2517</span> {</div> |
| <div class="line"><a name="l02518"></a><span class="lineno"> 2518</span>  <span class="keywordflow">if</span>(m_Graph->output_size() == 0)</div> |
| <div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>  {</div> |
| <div class="line"><a name="l02520"></a><span class="lineno"> 2520</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"The given model does not have any outputs {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02521"></a><span class="lineno"> 2521</span>  }</div> |
| <div class="line"><a name="l02522"></a><span class="lineno"> 2522</span>  </div> |
| <div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> outputIndex = 0; outputIndex < m_Graph->output_size(); ++outputIndex)</div> |
| <div class="line"><a name="l02524"></a><span class="lineno"> 2524</span>  {</div> |
| <div class="line"><a name="l02525"></a><span class="lineno"> 2525</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer =</div> |
| <div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>  m_Network->AddOutputLayer(<span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.html#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a><span class="keyword">></span>(outputIndex),</div> |
| <div class="line"><a name="l02527"></a><span class="lineno"> 2527</span>  m_Graph->output(outputIndex).name().c_str());</div> |
| <div class="line"><a name="l02528"></a><span class="lineno"> 2528</span>  </div> |
| <div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>  RegisterInputSlots(layer, { m_Graph->output(outputIndex).name() });</div> |
| <div class="line"><a name="l02530"></a><span class="lineno"> 2530</span>  }</div> |
| <div class="line"><a name="l02531"></a><span class="lineno"> 2531</span> }</div> |
| <div class="line"><a name="l02532"></a><span class="lineno"> 2532</span>  </div> |
| <div class="line"><a name="l02533"></a><span class="lineno"> 2533</span> <span class="keywordtype">void</span> OnnxParserImpl::RegisterInputSlot(<a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer,</div> |
| <div class="line"><a name="l02534"></a><span class="lineno"> 2534</span>  <span class="keyword">const</span> std::string& tensorId,</div> |
| <div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIndex)</div> |
| <div class="line"><a name="l02536"></a><span class="lineno"> 2536</span> {</div> |
| <div class="line"><a name="l02537"></a><span class="lineno"> 2537</span>  <a class="code" href="classarmnn_1_1_i_input_slot.html">armnn::IInputSlot</a>* slot = &(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(slotIndex));</div> |
| <div class="line"><a name="l02538"></a><span class="lineno"> 2538</span>  </div> |
| <div class="line"><a name="l02539"></a><span class="lineno"> 2539</span>  <span class="keyword">auto</span> it = m_TensorConnections.find(tensorId);</div> |
| <div class="line"><a name="l02540"></a><span class="lineno"> 2540</span>  </div> |
| <div class="line"><a name="l02541"></a><span class="lineno"> 2541</span>  <span class="keywordflow">if</span> (it == m_TensorConnections.end())</div> |
| <div class="line"><a name="l02542"></a><span class="lineno"> 2542</span>  {</div> |
| <div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>  <span class="comment">//First time seeing this tensor, we need to map it</span></div> |
| <div class="line"><a name="l02544"></a><span class="lineno"> 2544</span>  m_TensorConnections[tensorId] = TensorSlots();</div> |
| <div class="line"><a name="l02545"></a><span class="lineno"> 2545</span>  }</div> |
| <div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>  m_TensorConnections[tensorId].inputSlots.push_back(slot);</div> |
| <div class="line"><a name="l02547"></a><span class="lineno"> 2547</span> }</div> |
| <div class="line"><a name="l02548"></a><span class="lineno"> 2548</span>  </div> |
| <div class="line"><a name="l02549"></a><span class="lineno"> 2549</span> <span class="keywordtype">void</span> OnnxParserImpl::RegisterInputSlots(<a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer, <span class="keyword">const</span> std::vector<std::string>& tensorIds)</div> |
| <div class="line"><a name="l02550"></a><span class="lineno"> 2550</span> {</div> |
| <div class="line"><a name="l02551"></a><span class="lineno"> 2551</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>  {</div> |
| <div class="line"><a name="l02553"></a><span class="lineno"> 2553</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02554"></a><span class="lineno"> 2554</span>  }</div> |
| <div class="line"><a name="l02555"></a><span class="lineno"> 2555</span>  </div> |
| <div class="line"><a name="l02556"></a><span class="lineno"> 2556</span>  <span class="keywordflow">if</span> (tensorIds.size() != layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>())</div> |
| <div class="line"><a name="l02557"></a><span class="lineno"> 2557</span>  {</div> |
| <div class="line"><a name="l02558"></a><span class="lineno"> 2558</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l02559"></a><span class="lineno"> 2559</span>  fmt::format(<span class="stringliteral">"The number of tensor inputs ({}) does not match the number expected ({}) {}"</span>,</div> |
| <div class="line"><a name="l02560"></a><span class="lineno"> 2560</span>  tensorIds.size(),</div> |
| <div class="line"><a name="l02561"></a><span class="lineno"> 2561</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>(),</div> |
| <div class="line"><a name="l02562"></a><span class="lineno"> 2562</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02563"></a><span class="lineno"> 2563</span>  }</div> |
| <div class="line"><a name="l02564"></a><span class="lineno"> 2564</span>  </div> |
| <div class="line"><a name="l02565"></a><span class="lineno"> 2565</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIndex = 0; slotIndex < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>(); ++slotIndex)</div> |
| <div class="line"><a name="l02566"></a><span class="lineno"> 2566</span>  {</div> |
| <div class="line"><a name="l02567"></a><span class="lineno"> 2567</span>  std::string tensorId = tensorIds[slotIndex];</div> |
| <div class="line"><a name="l02568"></a><span class="lineno"> 2568</span>  <a class="code" href="classarmnn_1_1_i_input_slot.html">armnn::IInputSlot</a>* slot = &(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(slotIndex));</div> |
| <div class="line"><a name="l02569"></a><span class="lineno"> 2569</span>  </div> |
| <div class="line"><a name="l02570"></a><span class="lineno"> 2570</span>  <span class="keyword">auto</span> it = m_TensorConnections.find(tensorId);</div> |
| <div class="line"><a name="l02571"></a><span class="lineno"> 2571</span>  </div> |
| <div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>  <span class="keywordflow">if</span> (it == m_TensorConnections.end())</div> |
| <div class="line"><a name="l02573"></a><span class="lineno"> 2573</span>  {</div> |
| <div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>  <span class="comment">// First time seing this tensor, we need to map it</span></div> |
| <div class="line"><a name="l02575"></a><span class="lineno"> 2575</span>  m_TensorConnections[tensorId] = TensorSlots();</div> |
| <div class="line"><a name="l02576"></a><span class="lineno"> 2576</span>  }</div> |
| <div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>  m_TensorConnections[tensorId].inputSlots.push_back(slot);</div> |
| <div class="line"><a name="l02578"></a><span class="lineno"> 2578</span>  }</div> |
| <div class="line"><a name="l02579"></a><span class="lineno"> 2579</span> }</div> |
| <div class="line"><a name="l02580"></a><span class="lineno"> 2580</span>  </div> |
| <div class="line"><a name="l02581"></a><span class="lineno"> 2581</span> <span class="keywordtype">void</span> OnnxParserImpl::RegisterOutputSlots(<a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer, <span class="keyword">const</span> std::vector<std::string>& tensorIds)</div> |
| <div class="line"><a name="l02582"></a><span class="lineno"> 2582</span> {</div> |
| <div class="line"><a name="l02583"></a><span class="lineno"> 2583</span>  <span class="keywordflow">if</span> (!layer)</div> |
| <div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>  {</div> |
| <div class="line"><a name="l02585"></a><span class="lineno"> 2585</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">"Layer pointer is null {}"</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>  }</div> |
| <div class="line"><a name="l02587"></a><span class="lineno"> 2587</span>  </div> |
| <div class="line"><a name="l02588"></a><span class="lineno"> 2588</span>  <span class="keywordflow">if</span> (tensorIds.size() != layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>())</div> |
| <div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>  {</div> |
| <div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div> |
| <div class="line"><a name="l02591"></a><span class="lineno"> 2591</span>  fmt::format(<span class="stringliteral">"The number of tensor outputs ({}) does not match the number expected ({}) {} "</span>,</div> |
| <div class="line"><a name="l02592"></a><span class="lineno"> 2592</span>  tensorIds.size(),</div> |
| <div class="line"><a name="l02593"></a><span class="lineno"> 2593</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(),</div> |
| <div class="line"><a name="l02594"></a><span class="lineno"> 2594</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02595"></a><span class="lineno"> 2595</span>  }</div> |
| <div class="line"><a name="l02596"></a><span class="lineno"> 2596</span>  </div> |
| <div class="line"><a name="l02597"></a><span class="lineno"> 2597</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIndex = 0; slotIndex < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++slotIndex)</div> |
| <div class="line"><a name="l02598"></a><span class="lineno"> 2598</span>  {</div> |
| <div class="line"><a name="l02599"></a><span class="lineno"> 2599</span>  std::string tensorId = tensorIds[slotIndex];</div> |
| <div class="line"><a name="l02600"></a><span class="lineno"> 2600</span>  <a class="code" href="classarmnn_1_1_i_output_slot.html">armnn::IOutputSlot</a>* slot = &(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(slotIndex));</div> |
| <div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>  </div> |
| <div class="line"><a name="l02602"></a><span class="lineno"> 2602</span>  <span class="keyword">auto</span> it = m_TensorConnections.find(tensorId);</div> |
| <div class="line"><a name="l02603"></a><span class="lineno"> 2603</span>  </div> |
| <div class="line"><a name="l02604"></a><span class="lineno"> 2604</span>  <span class="keywordflow">if</span> (it == m_TensorConnections.end())</div> |
| <div class="line"><a name="l02605"></a><span class="lineno"> 2605</span>  {</div> |
| <div class="line"><a name="l02606"></a><span class="lineno"> 2606</span>  <span class="comment">//First time seing this tensor, we need to map it</span></div> |
| <div class="line"><a name="l02607"></a><span class="lineno"> 2607</span>  m_TensorConnections[tensorId] = TensorSlots();</div> |
| <div class="line"><a name="l02608"></a><span class="lineno"> 2608</span>  }</div> |
| <div class="line"><a name="l02609"></a><span class="lineno"> 2609</span>  </div> |
| <div class="line"><a name="l02610"></a><span class="lineno"> 2610</span>  TensorSlots& tensorSlots = m_TensorConnections[tensorId];</div> |
| <div class="line"><a name="l02611"></a><span class="lineno"> 2611</span>  </div> |
| <div class="line"><a name="l02612"></a><span class="lineno"> 2612</span>  <span class="comment">// assuming there is only one producer for that tensor</span></div> |
| <div class="line"><a name="l02613"></a><span class="lineno"> 2613</span>  <span class="keywordflow">if</span> (tensorSlots.outputSlot != <span class="keyword">nullptr</span>)</div> |
| <div class="line"><a name="l02614"></a><span class="lineno"> 2614</span>  {</div> |
| <div class="line"><a name="l02615"></a><span class="lineno"> 2615</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">"Another layer has already registered itself as the producer of "</span></div> |
| <div class="line"><a name="l02616"></a><span class="lineno"> 2616</span>  <span class="stringliteral">"tensor:{} {}"</span>,</div> |
| <div class="line"><a name="l02617"></a><span class="lineno"> 2617</span>  tensorId,</div> |
| <div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02619"></a><span class="lineno"> 2619</span>  }</div> |
| <div class="line"><a name="l02620"></a><span class="lineno"> 2620</span>  tensorSlots.outputSlot = slot;</div> |
| <div class="line"><a name="l02621"></a><span class="lineno"> 2621</span>  }</div> |
| <div class="line"><a name="l02622"></a><span class="lineno"> 2622</span>  </div> |
| <div class="line"><a name="l02623"></a><span class="lineno"> 2623</span> }</div> |
| <div class="line"><a name="l02624"></a><span class="lineno"> 2624</span>  </div> |
| <div class="line"><a name="l02625"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a8b053a6c449d0814cc831c916c126668"> 2625</a></span> <a class="code" href="namespacearmnn_onnx_parser.html#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a8b053a6c449d0814cc831c916c126668">OnnxParserImpl::GetNetworkInputBindingInfo</a>(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div> |
| <div class="line"><a name="l02626"></a><span class="lineno"> 2626</span> <span class="keyword"></span>{</div> |
| <div class="line"><a name="l02627"></a><span class="lineno"> 2627</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < m_Graph->input_size(); ++i)</div> |
| <div class="line"><a name="l02628"></a><span class="lineno"> 2628</span>  {</div> |
| <div class="line"><a name="l02629"></a><span class="lineno"> 2629</span>  <span class="keyword">auto</span> input = m_Graph->input(i);</div> |
| <div class="line"><a name="l02630"></a><span class="lineno"> 2630</span>  <span class="keywordflow">if</span>(input.name() == name)</div> |
| <div class="line"><a name="l02631"></a><span class="lineno"> 2631</span>  {</div> |
| <div class="line"><a name="l02632"></a><span class="lineno"> 2632</span>  <span class="keyword">auto</span> it = m_InputInfos.find(name);</div> |
| <div class="line"><a name="l02633"></a><span class="lineno"> 2633</span>  </div> |
| <div class="line"><a name="l02634"></a><span class="lineno"> 2634</span>  <span class="keywordflow">if</span> (it != m_InputInfos.end())</div> |
| <div class="line"><a name="l02635"></a><span class="lineno"> 2635</span>  {</div> |
| <div class="line"><a name="l02636"></a><span class="lineno"> 2636</span>  <span class="keywordflow">return</span> std::make_pair(<span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.html#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a><span class="keyword">></span>(i), it->second);</div> |
| <div class="line"><a name="l02637"></a><span class="lineno"> 2637</span>  }</div> |
| <div class="line"><a name="l02638"></a><span class="lineno"> 2638</span>  }</div> |
| <div class="line"><a name="l02639"></a><span class="lineno"> 2639</span>  }</div> |
| <div class="line"><a name="l02640"></a><span class="lineno"> 2640</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.html">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"The input layer '{}' does not exist {}"</span>,</div> |
| <div class="line"><a name="l02641"></a><span class="lineno"> 2641</span>  name, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02642"></a><span class="lineno"> 2642</span> }</div> |
| <div class="line"><a name="l02643"></a><span class="lineno"> 2643</span>  </div> |
| <div class="line"><a name="l02644"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a4b1fdcb1985af12dd1848a9ffa5d3271"> 2644</a></span> <a class="code" href="namespacearmnn_onnx_parser.html#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a4b1fdcb1985af12dd1848a9ffa5d3271">OnnxParserImpl::GetNetworkOutputBindingInfo</a>(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div> |
| <div class="line"><a name="l02645"></a><span class="lineno"> 2645</span> <span class="keyword"></span>{</div> |
| <div class="line"><a name="l02646"></a><span class="lineno"> 2646</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < m_Graph->output_size(); ++i)</div> |
| <div class="line"><a name="l02647"></a><span class="lineno"> 2647</span>  {</div> |
| <div class="line"><a name="l02648"></a><span class="lineno"> 2648</span>  <span class="keyword">auto</span> output = m_Graph->output(i);</div> |
| <div class="line"><a name="l02649"></a><span class="lineno"> 2649</span>  <span class="keywordflow">if</span>(output.name() == name)</div> |
| <div class="line"><a name="l02650"></a><span class="lineno"> 2650</span>  {</div> |
| <div class="line"><a name="l02651"></a><span class="lineno"> 2651</span>  <span class="keyword">auto</span> it = m_OutputInfos.find(name);</div> |
| <div class="line"><a name="l02652"></a><span class="lineno"> 2652</span>  </div> |
| <div class="line"><a name="l02653"></a><span class="lineno"> 2653</span>  <span class="keywordflow">if</span> (it != m_OutputInfos.end())</div> |
| <div class="line"><a name="l02654"></a><span class="lineno"> 2654</span>  {</div> |
| <div class="line"><a name="l02655"></a><span class="lineno"> 2655</span>  <span class="keywordflow">return</span> std::make_pair(<span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.html#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a><span class="keyword">></span>(i), it->second);</div> |
| <div class="line"><a name="l02656"></a><span class="lineno"> 2656</span>  }</div> |
| <div class="line"><a name="l02657"></a><span class="lineno"> 2657</span>  }</div> |
| <div class="line"><a name="l02658"></a><span class="lineno"> 2658</span>  }</div> |
| <div class="line"><a name="l02659"></a><span class="lineno"> 2659</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.html">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"The output layer '{}' does not exist {}"</span>,</div> |
| <div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>  name, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02661"></a><span class="lineno"> 2661</span> }</div> |
| <div class="line"><a name="l02662"></a><span class="lineno"> 2662</span>  </div> |
| <div class="line"><a name="l02663"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a7cf8b801043e1eccd5e6db1325eaa4fe"> 2663</a></span> std::vector<std::string> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a7cf8b801043e1eccd5e6db1325eaa4fe">OnnxParserImpl::GetInputs</a>(<a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a>& model)</div> |
| <div class="line"><a name="l02664"></a><span class="lineno"> 2664</span> {</div> |
| <div class="line"><a name="l02665"></a><span class="lineno"> 2665</span>  <span class="keywordflow">if</span>(model == <span class="keyword">nullptr</span>) {</div> |
| <div class="line"><a name="l02666"></a><span class="lineno"> 2666</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.html">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"The given model cannot be null {}"</span>,</div> |
| <div class="line"><a name="l02667"></a><span class="lineno"> 2667</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02668"></a><span class="lineno"> 2668</span>  }</div> |
| <div class="line"><a name="l02669"></a><span class="lineno"> 2669</span>  </div> |
| <div class="line"><a name="l02670"></a><span class="lineno"> 2670</span>  std::vector<std::string> inputNames;</div> |
| <div class="line"><a name="l02671"></a><span class="lineno"> 2671</span>  std::map<std::string, bool> isConstant;</div> |
| <div class="line"><a name="l02672"></a><span class="lineno"> 2672</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> tensor : model->graph().initializer())</div> |
| <div class="line"><a name="l02673"></a><span class="lineno"> 2673</span>  {</div> |
| <div class="line"><a name="l02674"></a><span class="lineno"> 2674</span>  isConstant[tensor.name()] = <span class="keyword">true</span>;</div> |
| <div class="line"><a name="l02675"></a><span class="lineno"> 2675</span>  }</div> |
| <div class="line"><a name="l02676"></a><span class="lineno"> 2676</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> input : model->graph().input())</div> |
| <div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>  {</div> |
| <div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>  <span class="keyword">auto</span> it = isConstant.find(input.name());</div> |
| <div class="line"><a name="l02679"></a><span class="lineno"> 2679</span>  <span class="keywordflow">if</span>(it == isConstant.end())</div> |
| <div class="line"><a name="l02680"></a><span class="lineno"> 2680</span>  {</div> |
| <div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>  inputNames.push_back(input.name());</div> |
| <div class="line"><a name="l02682"></a><span class="lineno"> 2682</span>  }</div> |
| <div class="line"><a name="l02683"></a><span class="lineno"> 2683</span>  }</div> |
| <div class="line"><a name="l02684"></a><span class="lineno"> 2684</span>  <span class="keywordflow">return</span> inputNames;</div> |
| <div class="line"><a name="l02685"></a><span class="lineno"> 2685</span> }</div> |
| <div class="line"><a name="l02686"></a><span class="lineno"> 2686</span>  </div> |
| <div class="line"><a name="l02687"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#ad116319e33228bc23ec505887d3eee4d"> 2687</a></span> std::vector<std::string> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#ad116319e33228bc23ec505887d3eee4d">OnnxParserImpl::GetOutputs</a>(<a class="code" href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">ModelPtr</a>& model)</div> |
| <div class="line"><a name="l02688"></a><span class="lineno"> 2688</span> {</div> |
| <div class="line"><a name="l02689"></a><span class="lineno"> 2689</span>  <span class="keywordflow">if</span>(model == <span class="keyword">nullptr</span>) {</div> |
| <div class="line"><a name="l02690"></a><span class="lineno"> 2690</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.html">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"The given model cannot be null {}"</span>,</div> |
| <div class="line"><a name="l02691"></a><span class="lineno"> 2691</span>  <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div> |
| <div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>  }</div> |
| <div class="line"><a name="l02693"></a><span class="lineno"> 2693</span>  </div> |
| <div class="line"><a name="l02694"></a><span class="lineno"> 2694</span>  std::vector<std::string> outputNames;</div> |
| <div class="line"><a name="l02695"></a><span class="lineno"> 2695</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> output : model->graph().output())</div> |
| <div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>  {</div> |
| <div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>  outputNames.push_back(output.name());</div> |
| <div class="line"><a name="l02698"></a><span class="lineno"> 2698</span>  }</div> |
| <div class="line"><a name="l02699"></a><span class="lineno"> 2699</span>  <span class="keywordflow">return</span> outputNames;</div> |
| <div class="line"><a name="l02700"></a><span class="lineno"> 2700</span> }</div> |
| <div class="line"><a name="l02701"></a><span class="lineno"> 2701</span>  </div> |
| <div class="line"><a name="l02702"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aa09a8bb02eed50715082d8b7fccd2f8d"> 2702</a></span> <span class="keyword">const</span> std::string <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aa09a8bb02eed50715082d8b7fccd2f8d">OnnxParserImpl::GetVersion</a>()</div> |
| <div class="line"><a name="l02703"></a><span class="lineno"> 2703</span> {</div> |
| <div class="line"><a name="l02704"></a><span class="lineno"> 2704</span>  <span class="keywordflow">return</span> <a class="code" href="include_2armnn_onnx_parser_2_version_8hpp.html#a91718cb27a114419c34ce33827e94321">ONNX_PARSER_VERSION</a>;</div> |
| <div class="line"><a name="l02705"></a><span class="lineno"> 2705</span> }</div> |
| <div class="line"><a name="l02706"></a><span class="lineno"> 2706</span>  </div> |
| <div class="line"><a name="l02707"></a><span class="lineno"> 2707</span> } <span class="comment">// namespace armnnOnnxParser</span></div> |
| </div><!-- fragment --></div><!-- contents --> |
| </div><!-- doc-content --> |
| <div class="ttc" id="a_assert_8hpp_html_a5698be69cbd5dfe6c28fcd9867e8cbed"><div class="ttname"><a href="_assert_8hpp.html#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a></div><div class="ttdeci">#define ARMNN_ASSERT(COND)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.html#l00014">Assert.hpp:14</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_batch_normalization_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.html">armnn::BatchNormalizationDescriptor</a></div><div class="ttdoc">A BatchNormalizationDescriptor for the BatchNormalizationLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00828">Descriptors.hpp:828</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_html_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html#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.html#l00570">Descriptors.hpp:570</a></div></div> |
| <div class="ttc" id="anamespacearmnn_html_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.html#l00339">INetwork.hpp:339</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_a8c29d6ea9b4186d69aad5961c910939c"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#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.html#l00425">Descriptors.hpp:425</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00198">Tensor.hpp:198</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_ad116319e33228bc23ec505887d3eee4d"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#ad116319e33228bc23ec505887d3eee4d">armnnOnnxParser::OnnxParserImpl::GetOutputs</a></div><div class="ttdeci">static std::vector< std::string > GetOutputs(ModelPtr &model)</div><div class="ttdoc">Retrieve outputs names.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l02687">OnnxParser.cpp:2687</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_activation_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.html">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00036">Descriptors.hpp:36</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_fully_connected_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.html">armnn::FullyConnectedDescriptor</a></div><div class="ttdoc">A FullyConnectedDescriptor for the FullyConnectedLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00507">Descriptors.hpp:507</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_tensor_info_html_abcbdfb544ece4c31d0b37715ad0f3be0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#abcbdfb544ece4c31d0b37715ad0f3be0">armnn::TensorInfo::GetNumBytes</a></div><div class="ttdeci">unsigned int GetNumBytes() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00427">Tensor.cpp:427</a></div></div> |
| <div class="ttc" id="a_onnx_parser_8cpp_html_a0e987f9d4f46b35c9b1ff0cc950dc5b1"><div class="ttname"><a href="_onnx_parser_8cpp.html#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a></div><div class="ttdeci">#define VALID_INPUTS(NODE, VALID_INPUTS)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00511">OnnxParser.cpp:511</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_optional_html"><div class="ttname"><a href="classarmnn_1_1_optional.html">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.html#l00270">Optional.hpp:270</a></div></div> |
| <div class="ttc" id="aclassarmnn_deserializer_1_1_i_deserializer_html_a85f0c438b389992a68adeb6af59f362d"><div class="ttname"><a href="classarmnn_deserializer_1_1_i_deserializer.html#a85f0c438b389992a68adeb6af59f362d">armnnDeserializer::IDeserializer::CreateRaw</a></div><div class="ttdeci">static IDeserializer * CreateRaw()</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8cpp_source.html#l00042">Deserializer.cpp:42</a></div></div> |
| <div class="ttc" id="anamespacearmnn_onnx_parser_html"><div class="ttname"><a href="namespacearmnn_onnx_parser.html">armnnOnnxParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_onnx_parser_8hpp_source.html#l00014">IOnnxParser.hpp:14</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_i_connectable_layer_html_a9c2cba04b6d7ace4fc2a2436b82a5a63"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.html#a9c2cba04b6d7ace4fc2a2436b82a5a63">armnn::IConnectableLayer::GetNumInputSlots</a></div><div class="ttdeci">virtual unsigned int GetNumInputSlots() const =0</div><div class="ttdoc">Returns the number of connectable input slots.</div></div> |
| <div class="ttc" id="a_descriptors_8hpp_html"><div class="ttname"><a href="_descriptors_8hpp.html">Descriptors.hpp</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a8b053a6c449d0814cc831c916c126668"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a8b053a6c449d0814cc831c916c126668">armnnOnnxParser::OnnxParserImpl::GetNetworkInputBindingInfo</a></div><div class="ttdeci">BindingPointInfo GetNetworkInputBindingInfo(const std::string &name) const</div><div class="ttdoc">Retrieve 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="_onnx_parser_8cpp_source.html#l02625">OnnxParser.cpp:2625</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_fully_connected_descriptor_html_a281fcaec86e17c97f7b8402633f6b55a"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.html#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.html#l00528">Descriptors.hpp:528</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::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.html#l00708">Descriptors.hpp:708</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_a5699e8606c37d18c03910b242cd1b010"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#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.html#l00417">Descriptors.hpp:417</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_i_connectable_layer_html_afcc1c3a20bd2860e0ddd21674389246f"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.html#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="astructarmnn_1_1_activation_descriptor_html_a017b2990003a014234f13e999dc7c689"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.html#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,...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00061">Descriptors.hpp:61</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_gather_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_gather_descriptor.html">armnn::GatherDescriptor</a></div><div class="ttdoc">A GatherDescriptor for the GatherLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00965">Descriptors.hpp:965</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00152">Tensor.hpp:152</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_acf9c6119ceb99755bc1f86c5a325c796"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#acf9c6119ceb99755bc1f86c5a325c796">armnnOnnxParser::OnnxParserImpl::LoadModelFromBinaryFile</a></div><div class="ttdeci">static ModelPtr LoadModelFromBinaryFile(const char *fileName)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00813">OnnxParser.cpp:813</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#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.html#l00421">Descriptors.hpp:421</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#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.html#l00197">Tensor.hpp:197</a></div></div> |
| <div class="ttc" id="a_exceptions_8hpp_html_aa3be76aec4ce713822a5ea1ecbb7bc61"><div class="ttname"><a href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a></div><div class="ttdeci">#define CHECK_LOCATION()</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00203">Exceptions.hpp:203</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a4b1fdcb1985af12dd1848a9ffa5d3271"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a4b1fdcb1985af12dd1848a9ffa5d3271">armnnOnnxParser::OnnxParserImpl::GetNetworkOutputBindingInfo</a></div><div class="ttdeci">BindingPointInfo GetNetworkOutputBindingInfo(const std::string &name) const</div><div class="ttdoc">Retrieve binding info (layer id and tensor info) for the network output identified by the given layer...</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l02644">OnnxParser.cpp:2644</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a32a96909bc8a8ee9076bd4d5c1028301"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a32a96909bc8a8ee9076bd4d5c1028301">armnnOnnxParser::OnnxParserImpl::CreateNetworkFromBinary</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromBinary(const std::vector< uint8_t > &binaryContent)</div><div class="ttdoc">Create the network from a protobuf binary.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00775">OnnxParser.cpp:775</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#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.html#l00692">Descriptors.hpp:692</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_html_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html#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.html#l00576">Descriptors.hpp:576</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#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.html#l00411">Descriptors.hpp:411</a></div></div> |
| <div class="ttc" id="a_deprecated_8hpp_html_ab66a241a0ed3ee89c866e777b035d0ed"><div class="ttname"><a href="_deprecated_8hpp.html#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_BEGIN</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.html#l00033">Deprecated.hpp:33</a></div></div> |
| <div class="ttc" id="anamespacearmnn_utils_html_a523deabeb7d0a884028b35eebfd1cb6c"><div class="ttname"><a href="namespacearmnn_utils.html#a523deabeb7d0a884028b35eebfd1cb6c">armnnUtils::ProcessConcatInputTensorInfo</a></div><div class="ttdeci">void ProcessConcatInputTensorInfo(armnn::TensorInfo &inputTensorInfo, armnn::OriginsDescriptor &concatDescriptor, const unsigned int &concatAxis, unsigned int inputIndex, unsigned int &mergeDimOrigin)</div><div class="ttdef"><b>Definition:</b> <a href="_parser_helper_8cpp_source.html#l00019">ParserHelper.cpp:19</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a30c0c947bb15e86ee6d535ecd934c0a6"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a30c0c947bb15e86ee6d535ecd934c0a6">armnnOnnxParser::OnnxParserImpl::CreateNetworkFromString</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromString(const std::string &protoText)</div><div class="ttdoc">Create the network directly from protobuf text in a string. Useful for debugging/testing.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00876">OnnxParser.cpp:876</a></div></div> |
| <div class="ttc" id="anamespacearmnn_utils_html_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &dstShape, const armnn::PermutationVector &mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.html#l00164">Permute.cpp:164</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_i_connectable_layer_html_aa6e3c075c888e7c16942a468a3aae33c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.html#aa6e3c075c888e7c16942a468a3aae33c">armnn::IConnectableLayer::InferOutputShapes</a></div><div class="ttdeci">virtual std::vector< TensorShape > InferOutputShapes(const std::vector< TensorShape > &inputShapes) const =0</div><div class="ttdoc">Infer the shape of the output(s) based on the provided input shape(s)</div></div> |
| <div class="ttc" id="anamespacearmnn_utils_html_abeaf4f6785039866fd075f4569ba8e84"><div class="ttname"><a href="namespacearmnn_utils.html#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a></div><div class="ttdeci">armnn::TensorShape Permuted(const armnn::TensorShape &srcShape, const armnn::PermutationVector &mappings)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.html#l00125">Permute.cpp:125</a></div></div> |
| <div class="ttc" id="ainclude_2armnn_onnx_parser_2_version_8hpp_html_a91718cb27a114419c34ce33827e94321"><div class="ttname"><a href="include_2armnn_onnx_parser_2_version_8hpp.html#a91718cb27a114419c34ce33827e94321">ONNX_PARSER_VERSION</a></div><div class="ttdeci">#define ONNX_PARSER_VERSION</div><div class="ttdoc">ONNX_PARSER_VERSION: "X.Y.Z" where: X = Major version number Y = Minor version number Z = Patch versi...</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_onnx_parser_2_version_8hpp_source.html#l00025">Version.hpp:25</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_a6d8fb685cc1ff224f25aa127fcf62c86"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#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.html#l00415">Descriptors.hpp:415</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_html_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html#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.html#l00566">Descriptors.hpp:566</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::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.html#l00702">Descriptors.hpp:702</a></div></div> |
| <div class="ttc" id="anamespacearmnn_onnx_parser_html_a503ae4f55dae1486e53978657083b35d"><div class="ttname"><a href="namespacearmnn_onnx_parser.html#a503ae4f55dae1486e53978657083b35d">armnnOnnxParser::ModelPtr</a></div><div class="ttdeci">std::unique_ptr< onnx::ModelProto > ModelPtr</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8hpp_source.html#l00023">OnnxParser.hpp:23</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_check_location_html_a5e3562cda960da001597e7dd5679b140"><div class="ttname"><a href="structarmnn_1_1_check_location.html#a5e3562cda960da001597e7dd5679b140">armnn::CheckLocation::AsString</a></div><div class="ttdeci">std::string AsString() const</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00029">Exceptions.hpp:29</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_html_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html#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.html#l00580">Descriptors.hpp:580</a></div></div> |
| <div class="ttc" id="anamespacearmnn_html_a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282"><div class="ttname"><a href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">armnn::BoostLogSeverityMapping::error</a></div><div class="ttdeci">@ error</div></div> |
| <div class="ttc" id="aclassarmnn_1_1_i_connectable_layer_html_ac2dac3b61c94de52093616be4ab17f8d"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.html#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="a_numeric_cast_8hpp_html"><div class="ttname"><a href="_numeric_cast_8hpp.html">NumericCast.hpp</a></div></div> |
| <div class="ttc" id="a_assert_8hpp_html"><div class="ttname"><a href="_assert_8hpp.html">Assert.hpp</a></div></div> |
| <div class="ttc" id="a_verification_helpers_8hpp_html_aaef93dc9a69f51b59f3cdd0ff0165927"><div class="ttname"><a href="_verification_helpers_8hpp.html#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a></div><div class="ttdeci">#define CHECKED_NON_NEGATIVE(VALUE)</div><div class="ttdef"><b>Definition:</b> <a href="_verification_helpers_8hpp_source.html#l00035">VerificationHelpers.hpp:35</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_tensor_shape_html"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00020">Tensor.hpp:20</a></div></div> |
| <div class="ttc" id="a_verification_helpers_8hpp_html"><div class="ttname"><a href="_verification_helpers_8hpp.html">VerificationHelpers.hpp</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_i_output_slot_html"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.html">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.html#l00053">INetwork.hpp:53</a></div></div> |
| <div class="ttc" id="a_onnx_parser_8cpp_html_a71cae957feb9162183d6f62fd549ffe1"><div class="ttname"><a href="_onnx_parser_8cpp.html#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a></div><div class="ttdeci">#define CHECK_VALID_DATATYPE(NODE, TENSOR, ACTUAL,...)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00129">OnnxParser.cpp:129</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_check_location_html"><div class="ttname"><a href="structarmnn_1_1_check_location.html">armnn::CheckLocation</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00014">Exceptions.hpp:14</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_tensor_shape_html_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdoc">Function that returns the tensor rank.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00174">Tensor.cpp:174</a></div></div> |
| <div class="ttc" id="a_verification_helpers_8hpp_html_aa693ef8620e450b6362938828002f2a6"><div class="ttname"><a href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a></div><div class="ttdeci">#define CHECKED_INT32(VALUE)</div><div class="ttdef"><b>Definition:</b> <a href="_verification_helpers_8hpp_source.html#l00030">VerificationHelpers.hpp:30</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#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.html#l00413">Descriptors.hpp:413</a></div></div> |
| <div class="ttc" id="a_onnx_parser_8hpp_html"><div class="ttname"><a href="_onnx_parser_8hpp.html">OnnxParser.hpp</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#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.html#l00409">Descriptors.hpp:409</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_fully_connected_descriptor_html_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.html#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.html#l00526">Descriptors.hpp:526</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_i_output_slot_html_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</div></div> |
| <div class="ttc" id="astructarmnn_1_1_transpose_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_transpose_descriptor.html">armnn::TransposeDescriptor</a></div><div class="ttdoc">A TransposeDescriptor for the TransposeLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01490">Descriptors.hpp:1490</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_aed935c554e4f6a4e7b9dcde057d00877"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aed935c554e4f6a4e7b9dcde057d00877">armnnOnnxParser::OnnxParserImpl::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile)</div><div class="ttdoc">Create the network from a protobuf binary file on disk.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00841">OnnxParser.cpp:841</a></div></div> |
| <div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00048">Types.hpp:48</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_html_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html#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.html#l00582">Descriptors.hpp:582</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_reshape_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.html">armnn::ReshapeDescriptor</a></div><div class="ttdoc">A ReshapeDescriptor for the ReshapeLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01023">Descriptors.hpp:1023</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_invalid_argument_exception_html"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.html">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00080">Exceptions.hpp:80</a></div></div> |
| <div class="ttc" id="anamespacearmnn_html_ab8cf8f9fb6792e654c2d8d8382f6f01b"><div class="ttname"><a href="namespacearmnn.html#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.html#l00309">Types.hpp:309</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::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.html#l00694">Descriptors.hpp:694</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_activation_descriptor_html_af10fa7883e3579950f477bee92a64844"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.html#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,...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00059">Descriptors.hpp:59</a></div></div> |
| <div class="ttc" id="ainclude_2armnn_onnx_parser_2_version_8hpp_html"><div class="ttname"><a href="include_2armnn_onnx_parser_2_version_8hpp.html">Version.hpp</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_permute_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_permute_descriptor.html">armnn::PermuteDescriptor</a></div><div class="ttdoc">A PermuteDescriptor for the PermuteLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00149">Descriptors.hpp:149</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_gather_descriptor_html_a35d11c7d509d1adbae1ae01c58394a7f"><div class="ttname"><a href="structarmnn_1_1_gather_descriptor.html#a35d11c7d509d1adbae1ae01c58394a7f">armnn::GatherDescriptor::m_Axis</a></div><div class="ttdeci">int32_t m_Axis</div><div class="ttdoc">The axis in params to gather indices from.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00981">Descriptors.hpp:981</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_html_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::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.html#l00572">Descriptors.hpp:572</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_permutation_vector_html"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00314">Types.hpp:314</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_reshape_descriptor_html_a1178f4dafdda81f59c15145ec327f7d9"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.html#a1178f4dafdda81f59c15145ec327f7d9">armnn::ReshapeDescriptor::m_TargetShape</a></div><div class="ttdeci">TensorShape m_TargetShape</div><div class="ttdoc">Target shape value.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01039">Descriptors.hpp:1039</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a8e30b9dff215c314959ca3145e939338"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a8e30b9dff215c314959ca3145e939338">armnnOnnxParser::OnnxParserImpl::LoadModelFromBinary</a></div><div class="ttdeci">static ModelPtr LoadModelFromBinary(const std::vector< uint8_t > &binaryContent)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00791">OnnxParser.cpp:791</a></div></div> |
| <div class="ttc" id="a_parser_helper_8hpp_html"><div class="ttname"><a href="_parser_helper_8hpp.html">ParserHelper.hpp</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a181f87cf45fdc9f040a41c985ce7f8cd"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a181f87cf45fdc9f040a41c985ce7f8cd">armnnOnnxParser::OnnxParserImpl::LoadModelFromString</a></div><div class="ttdeci">static ModelPtr LoadModelFromString(const std::string &inputString)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00857">OnnxParser.cpp:857</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#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.html#l00407">Descriptors.hpp:407</a></div></div> |
| <div class="ttc" id="a_permute_8hpp_html"><div class="ttname"><a href="_permute_8hpp.html">Permute.hpp</a></div></div> |
| <div class="ttc" id="anamespacearmnn_html_a56297e0f7b215eea46c818cb7528d9ea"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a></div><div class="ttdeci">ActivationFunction</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00086">Types.hpp:86</a></div></div> |
| <div class="ttc" id="anamespacearmnn_html_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div><div class="ttdeci">@ info</div></div> |
| <div class="ttc" id="a_onnx_parser_8cpp_html_a5426a7adb280d1739cc6d66fe9ac1b9c"><div class="ttname"><a href="_onnx_parser_8cpp.html#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a></div><div class="ttdeci">#define STR_LIST(...)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00133">OnnxParser.cpp:133</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_aa09a8bb02eed50715082d8b7fccd2f8d"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aa09a8bb02eed50715082d8b7fccd2f8d">armnnOnnxParser::OnnxParserImpl::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="_onnx_parser_8cpp_source.html#l02702">OnnxParser.cpp:2702</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_html_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html#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.html#l00574">Descriptors.hpp:574</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_html_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html#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.html#l00568">Descriptors.hpp:568</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00534">Descriptors.hpp:534</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::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.html#l00698">Descriptors.hpp:698</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#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.html#l00419">Descriptors.hpp:419</a></div></div> |
| <div class="ttc" id="anamespacearmnn_deserializer_html_a948b8c615ff06defa3b80d2352259ff2"><div class="ttname"><a href="namespacearmnn_deserializer.html#a948b8c615ff06defa3b80d2352259ff2">armnnDeserializer::ToTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo ToTensorInfo(TensorRawPtr tensorPtr)</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8cpp_source.html#l00676">Deserializer.cpp:676</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_permutation_vector_html_a490ec6b59006d1fe1ec2ea30e69fb97c"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">armnn::PermutationVector::GetSize</a></div><div class="ttdeci">SizeType GetSize() const</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00357">Types.hpp:357</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_transpose_descriptor_html_a14433af2b223695b40d8c8f8ba2ebb8f"><div class="ttname"><a href="structarmnn_1_1_transpose_descriptor.html#a14433af2b223695b40d8c8f8ba2ebb8f">armnn::TransposeDescriptor::m_DimMappings</a></div><div class="ttdeci">PermutationVector m_DimMappings</div><div class="ttdoc">Indicates how to translate tensor elements from a given source into the target destination,...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01514">Descriptors.hpp:1514</a></div></div> |
| <div class="ttc" id="anamespacearmnn_onnx_parser_html_af6e5ebe4434a071057653025c4bb821b"><div class="ttname"><a href="namespacearmnn_onnx_parser.html#af6e5ebe4434a071057653025c4bb821b">armnnOnnxParser::CreateConstTensorImpl</a></div><div class="ttdeci">std::pair< armnn::ConstTensor, std::unique_ptr< T[]> > CreateConstTensorImpl(const T *bufferPtr, armnn::TensorInfo &tensorInfo, const armnn::Optional< armnn::PermutationVector & > permutationVector)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00604">OnnxParser.cpp:604</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_i_output_slot_html_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</div></div> |
| <div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00193">Tensor.hpp:193</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_html_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html#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.html#l00578">Descriptors.hpp:578</a></div></div> |
| <div class="ttc" id="a_deprecated_8hpp_html_ad762b11b48e5c1d1c1743f529485728a"><div class="ttname"><a href="_deprecated_8hpp.html#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_END</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.html#l00034">Deprecated.hpp:34</a></div></div> |
| <div class="ttc" id="anamespacestd_html"><div class="ttname"><a href="namespacestd.html">std</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.html#l00149">BackendId.hpp:149</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_parse_exception_html"><div class="ttname"><a href="classarmnn_1_1_parse_exception.html">armnn::ParseException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00092">Exceptions.hpp:92</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_i_onnx_parser_html"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_i_onnx_parser.html">armnnOnnxParser::IOnnxParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_onnx_parser_8hpp_source.html#l00023">IOnnxParser.hpp:23</a></div></div> |
| <div class="ttc" id="anamespacearmnn_onnx_parser_html_a9084adbf804022c874039ad40d1939e9"><div class="ttname"><a href="namespacearmnn_onnx_parser.html#a9084adbf804022c874039ad40d1939e9">armnnOnnxParser::BindingPointInfo</a></div><div class="ttdeci">armnn::BindingPointInfo BindingPointInfo</div><div class="ttdef"><b>Definition:</b> <a href="_i_onnx_parser_8hpp_source.html#l00017">IOnnxParser.hpp:17</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_aaf4ce461aa35597cf80954314a3cb0e1"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aaf4ce461aa35597cf80954314a3cb0e1">armnnOnnxParser::OnnxParserImpl::CreateNetworkFromTextFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromTextFile(const char *graphFile)</div><div class="ttdoc">Create the network from a protobuf text file on disk.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00759">OnnxParser.cpp:759</a></div></div> |
| <div class="ttc" id="anamespacearmnn_onnx_parser_html_ac7dfccab29feeb5f33f1ec0183c1e123"><div class="ttname"><a href="namespacearmnn_onnx_parser.html#ac7dfccab29feeb5f33f1ec0183c1e123">armnnOnnxParser::IOnnxParserPtr</a></div><div class="ttdeci">std::unique_ptr< IOnnxParser, void(*)(IOnnxParser *parser)> IOnnxParserPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_onnx_parser_8hpp_source.html#l00021">IOnnxParser.hpp:21</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_origins_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.html">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00201">Descriptors.hpp:201</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_tensor_info_html_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00195">Tensor.hpp:195</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_i_connectable_layer_html_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & 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="anamespacearmnn_html"><div class="ttname"><a href="namespacearmnn.html">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors.</div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.html#l00006">01_00_quick_start.dox:6</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_i_connectable_layer_html_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & 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="a_verification_helpers_8hpp_html_a479b2821a7a2cbb8fa8eb7f60a47065d"><div class="ttname"><a href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a></div><div class="ttdeci">#define CHECK_VALID_SIZE(ACTUAL,...)</div><div class="ttdef"><b>Definition:</b> <a href="_verification_helpers_8hpp_source.html#l00032">VerificationHelpers.hpp:32</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_activation_descriptor_html_a28c4c9cb15f6be3499abbc46b356060b"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.html#a28c4c9cb15f6be3499abbc46b356060b">armnn::ActivationDescriptor::m_B</a></div><div class="ttdeci">float m_B</div><div class="ttdoc">Beta lower bound value used by the activation functions. (BoundedReLu, Linear, TanH).</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00063">Descriptors.hpp:63</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_const_tensor_html"><div class="ttname"><a href="classarmnn_1_1_const_tensor.html">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00329">Tensor.hpp:329</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_i_connectable_layer_html"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.html">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.html#l00080">INetwork.hpp:80</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_i_input_slot_html"><div class="ttname"><a href="classarmnn_1_1_i_input_slot.html">armnn::IInputSlot</a></div><div class="ttdoc">An input connection slot for a layer.</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.html#l00025">INetwork.hpp:25</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_affb5b68b3eba3ed45a06c7cde7781962"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#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.html#l00423">Descriptors.hpp:423</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a7cf8b801043e1eccd5e6db1325eaa4fe"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a7cf8b801043e1eccd5e6db1325eaa4fe">armnnOnnxParser::OnnxParserImpl::GetInputs</a></div><div class="ttdeci">static std::vector< std::string > GetInputs(ModelPtr &model)</div><div class="ttdoc">Retrieve inputs names.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l02663">OnnxParser.cpp:2663</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8ffca1e21bdfa7f945617acd606aac91"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8ffca1e21bdfa7f945617acd606aac91">armnn::TensorInfo::SetConstant</a></div><div class="ttdeci">void SetConstant(const bool IsConstant=true)</div><div class="ttdoc">Marks the data corresponding to this tensor info as constant.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00518">Tensor.cpp:518</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00371">Descriptors.hpp:371</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00659">Descriptors.hpp:659</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_batch_normalization_descriptor_html_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.html#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.html#l00841">Descriptors.hpp:841</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_null_pointer_exception_html"><div class="ttname"><a href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00146">Exceptions.hpp:146</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_tensor_shape_html_a5a212540c00931bd2a4b4041beda33ae"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.html#a5a212540c00931bd2a4b4041beda33ae">armnn::TensorShape::GetDimensionality</a></div><div class="ttdeci">Dimensionality GetDimensionality() const</div><div class="ttdoc">Function that returns the tensor type.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00092">Tensor.hpp:92</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_tensor_shape_html_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.html#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorShape::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdoc">Function that calculates the tensor elements by multiplying all dimension size which are Specified.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00181">Tensor.cpp:181</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_optional_reference_switch_html_a77c7d528ac063d870b8c8426ec81c1c3"><div class="ttname"><a href="classarmnn_1_1_optional_reference_switch.html#a77c7d528ac063d870b8c8426ec81c1c3">armnn::OptionalReferenceSwitch< std::is_reference< T >::value, T >::value</a></div><div class="ttdeci">const T & value() const</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.html#l00146">Optional.hpp:146</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_a0031997bf43bd2747656c31e4977793a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#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.html#l00405">Descriptors.hpp:405</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_optional_base_html_a86b749ce2c4bc627fa8a1fcfaf0e314f"><div class="ttname"><a href="classarmnn_1_1_optional_base.html#a86b749ce2c4bc627fa8a1fcfaf0e314f">armnn::OptionalBase::has_value</a></div><div class="ttdeci">bool has_value() const noexcept</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.html#l00053">Optional.hpp:53</a></div></div> |
| <div class="ttc" id="aclassarmnn_1_1_file_not_found_exception_html"><div class="ttname"><a href="classarmnn_1_1_file_not_found_exception.html">armnn::FileNotFoundException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00086">Exceptions.hpp:86</a></div></div> |
| <div class="ttc" id="anamespacearmnn_deserializer_html_a7e75f47f676327bce37149932aa4a011"><div class="ttname"><a href="namespacearmnn_deserializer.html#a7e75f47f676327bce37149932aa4a011">armnnDeserializer::Pooling2dDescriptor</a></div><div class="ttdeci">const armnnSerializer::Pooling2dDescriptor * Pooling2dDescriptor</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8hpp_source.html#l00021">Deserializer.hpp:21</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a975a79b9b35d51ea81c42c05d245e7c0"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a975a79b9b35d51ea81c42c05d245e7c0">armnnOnnxParser::OnnxParserImpl::LoadModelFromTextFile</a></div><div class="ttdeci">static ModelPtr LoadModelFromTextFile(const char *fileName)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00734">OnnxParser.cpp:734</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::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.html#l00700">Descriptors.hpp:700</a></div></div> |
| <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::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.html#l00696">Descriptors.hpp:696</a></div></div> |
| <div class="ttc" id="aclassarmnn_onnx_parser_1_1_i_onnx_parser_html_a6bf5861864c8828e59df24a7868c5439"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#a6bf5861864c8828e59df24a7868c5439">armnnOnnxParser::IOnnxParser::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile)</div><div class="ttdoc">Create the network from a protobuf binary file on disk.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00050">OnnxParser.cpp:50</a></div></div> |
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