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<div class="title">Decoders.hpp</div> </div>
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<a href="_decoders_8hpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_base_iterator_8hpp.html">BaseIterator.hpp</a>&quot;</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_floating_point_converter_8hpp.html">armnnUtils/FloatingPointConverter.hpp</a>&gt;</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_tensor_utils_8hpp.html">armnnUtils/TensorUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="preprocessor">#include &lt;boost/assert.hpp&gt;</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;{</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;{</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="keyword">inline</span> std::unique_ptr&lt;Decoder&lt;float&gt;&gt; MakeSigned32PerAxisDecoder(<span class="keyword">const</span> TensorInfo&amp; info, <span class="keyword">const</span> <span class="keywordtype">void</span>* data)</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;{</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160; <span class="keyword">auto</span> params = <a class="code" href="namespacearmnn_utils.html#a1826e433f7e6817976a8175b4ef8296c">armnnUtils::GetPerAxisParams</a>(info);</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160; <span class="keywordflow">return</span> std::make_unique&lt;ScaledInt32PerAxisDecoder&gt;(</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160; <span class="keyword">static_cast&lt;</span><span class="keyword">const </span>int32_t*<span class="keyword">&gt;</span>(data),</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160; params.second,</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160; params.first);</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;}</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;<span class="keyword">inline</span> std::unique_ptr&lt;Decoder&lt;float&gt;&gt; MakeSigned32Decoder(<span class="keyword">const</span> TensorInfo&amp; info, <span class="keyword">const</span> <span class="keywordtype">void</span>* data)</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;{</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; <span class="keywordflow">if</span>(info.HasMultipleQuantizationScales())</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; <span class="comment">// NOTE: If we have multiple quantization scales, we create a ScaledInt32PerAxisDecoder.</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; <span class="comment">// This will be used to decode per-axis quantized convolution biases.</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; <span class="keywordflow">return</span> MakeSigned32PerAxisDecoder(info, data);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; }</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; <span class="keywordflow">if</span> (info.GetQuantizationDim().has_value())</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; {</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; <span class="comment">// NOTE: Even though we only have a single quantization scale, if the quantization</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <span class="comment">// dimension is set, the tensor has per-axis quantization and we need to create a</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; <span class="comment">// ScaledInt32PerAxisDecoder</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <span class="keywordflow">return</span> MakeSigned32PerAxisDecoder(info, data);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; }</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> scale = info.GetQuantizationScale();</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <span class="keywordflow">if</span> (scale == 0.f)</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; <span class="comment">// NOTE:: If no quantization scale is set, we create an Int32Decoder, which simply</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; <span class="comment">// casts the int value to float. This will be used for any INT32 data other than</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; <span class="comment">// convolution biases.</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; <span class="keywordflow">return</span> std::make_unique&lt;Int32Decoder&gt;(<span class="keyword">static_cast&lt;</span><span class="keyword">const </span>int32_t*<span class="keyword">&gt;</span>(data));</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; }</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; <span class="comment">// NOTE: If we only have a single (non-zero) quantization scale and no quantization</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; <span class="comment">// dimension is specified, we need to create a ScaledInt32Decoder. This will be used</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <span class="comment">// to decode per-tensor quantized convolution biases.</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <span class="keywordflow">return</span> std::make_unique&lt;ScaledInt32Decoder&gt;(<span class="keyword">static_cast&lt;</span><span class="keyword">const </span>int32_t*<span class="keyword">&gt;</span>(data), scale);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; }</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160;}</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160;} <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;<span class="keyword">inline</span> std::unique_ptr&lt;Decoder&lt;T&gt;&gt; <a class="code" href="namespacearmnn.html#a1545cb162c5a64d75d9c0c05e8ea387c">MakeDecoder</a>(<span class="keyword">const</span> TensorInfo&amp; info, <span class="keyword">const</span> <span class="keywordtype">void</span>* data = <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160;<span class="keyword">template</span>&lt;&gt;</div><div class="line"><a name="l00070"></a><span class="lineno"><a class="line" href="namespacearmnn.html#adb59a379c467b6d48874e946183b4d21"> 70</a></span>&#160;<span class="keyword">inline</span> std::unique_ptr&lt;Decoder&lt;float&gt;&gt; <a class="code" href="namespacearmnn.html#a1545cb162c5a64d75d9c0c05e8ea387c">MakeDecoder</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; info, <span class="keyword">const</span> <span class="keywordtype">void</span>* data)</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160;{</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; <span class="keywordflow">switch</span>(info.<a class="code" href="classarmnn_1_1_tensor_info.html#aea909c7327109228ef618d459015def3">GetDataType</a>())</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; {</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <a class="code" href="_deprecated_8hpp.html#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a34346ec9593088efe3a29c0dad92166d">armnn::DataType::QuantizedSymm8PerAxis</a>:</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; std::pair&lt;unsigned int, std::vector&lt;float&gt;&gt; params = <a class="code" href="namespacearmnn_utils.html#a1826e433f7e6817976a8175b4ef8296c">armnnUtils::GetPerAxisParams</a>(info);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <span class="keywordflow">return</span> std::make_unique&lt;QSymm8PerAxisDecoder&gt;(</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; <span class="keyword">static_cast&lt;</span><span class="keyword">const </span>int8_t*<span class="keyword">&gt;</span>(data),</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; params.second,</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; params.first);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; }</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; <a class="code" href="_deprecated_8hpp.html#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>:</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; {</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; <span class="keywordflow">return</span> std::make_unique&lt;QASymmS8Decoder&gt;(</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; <span class="keyword">static_cast&lt;</span><span class="keyword">const </span>int8_t*<span class="keyword">&gt;</span>(data),</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; info.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; info.<a class="code" href="classarmnn_1_1_tensor_info.html#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>());</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; }</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>:</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; {</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; <span class="keywordflow">return</span> std::make_unique&lt;QASymm8Decoder&gt;(</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; <span class="keyword">static_cast&lt;</span><span class="keyword">const </span>uint8_t*<span class="keyword">&gt;</span>(data),</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; info.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; info.<a class="code" href="classarmnn_1_1_tensor_info.html#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>());</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; }</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>:</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; {</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; <span class="keywordflow">return</span> std::make_unique&lt;QSymm16Decoder&gt;(</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; <span class="keyword">static_cast&lt;</span><span class="keyword">const </span>int16_t*<span class="keyword">&gt;</span>(data),</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; info.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; info.<a class="code" href="classarmnn_1_1_tensor_info.html#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>());</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; }</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>:</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; {</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <span class="keywordflow">return</span> std::make_unique&lt;Float16Decoder&gt;(<span class="keyword">static_cast&lt;</span><span class="keyword">const </span><a class="code" href="namespacearmnn.html#a0f38fa92b2468d5378258a2b074c1a31">Half</a>*<span class="keyword">&gt;</span>(data));</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; }</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>:</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; {</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <span class="keywordflow">return</span> std::make_unique&lt;Float32Decoder&gt;(<span class="keyword">static_cast&lt;</span><span class="keyword">const </span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(data));</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; }</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>:</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; {</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; <span class="keywordflow">return</span> MakeSigned32Decoder(info, data);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; }</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>:</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; {</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; <span class="keywordflow">if</span> (info.<a class="code" href="classarmnn_1_1_tensor_info.html#ab85cd8cc10c96a7c99c14042c251fc48">HasPerAxisQuantization</a>())</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; {</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; std::pair&lt;unsigned int, std::vector&lt;float&gt;&gt; params = <a class="code" href="namespacearmnn_utils.html#a1826e433f7e6817976a8175b4ef8296c">armnnUtils::GetPerAxisParams</a>(info);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; <span class="keywordflow">return</span> std::make_unique&lt;QSymm8PerAxisDecoder&gt;(</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; <span class="keyword">static_cast&lt;</span><span class="keyword">const </span>int8_t*<span class="keyword">&gt;</span>(data),</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; params.second,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; params.first);</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; }</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; {</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <span class="keywordflow">return</span> std::make_unique&lt;QSymmS8Decoder&gt;(</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; <span class="keyword">static_cast&lt;</span><span class="keyword">const </span>int8_t*<span class="keyword">&gt;</span>(data),</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; info.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; info.<a class="code" href="classarmnn_1_1_tensor_info.html#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>());</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; }</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; }</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; <span class="keywordflow">default</span>:</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; {</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; BOOST_ASSERT_MSG(<span class="keyword">false</span>, <span class="stringliteral">&quot;Unsupported Data Type!&quot;</span>);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; }</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; }</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160;}</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160;} <span class="comment">//namespace armnn</span></div><div class="ttc" id="classarmnn_1_1_tensor_info_html_ab85cd8cc10c96a7c99c14042c251fc48"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#ab85cd8cc10c96a7c99c14042c251fc48">armnn::TensorInfo::HasPerAxisQuantization</a></div><div class="ttdeci">bool HasPerAxisQuantization() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00232">Tensor.cpp:232</a></div></div>
<div class="ttc" id="namespacearmnn_html_a0f38fa92b2468d5378258a2b074c1a31"><div class="ttname"><a href="namespacearmnn.html#a0f38fa92b2468d5378258a2b074c1a31">armnn::Half</a></div><div class="ttdeci">half_float::half Half</div><div class="ttdef"><b>Definition:</b> <a href="_half_8hpp_source.html#l00016">Half.hpp:16</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_html_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00264">Tensor.cpp:264</a></div></div>
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<div class="ttc" id="namespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
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<div class="ttc" id="namespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div>
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<div class="ttc" id="classarmnn_1_1_tensor_info_html_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00247">Tensor.cpp:247</a></div></div>
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