<!-- HTML header for doxygen 1.8.14-->
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
<meta name="generator" content="Doxygen 1.8.11"/>
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<title>Caffe2 - C++ API: caffe2/core/blob_serialization.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<link rel="icon" href="/static/favicon.png" type="image/x-icon">
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
<script type="text/javascript">
$(document).ready(function() { init_search(); });
</script>
<link href="stylesheet.css" rel="stylesheet" type="text/css" />
<link href="main.css" rel="stylesheet" type="text/css"/>
</head>
<body>
<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
<div id="titlearea">
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
<td id="projectlogo" width="56"><a href="/"><img alt="Logo" src="Caffe2-with-name-55-tall.png"/></a></td>
<td id="projectalign" style="padding-left: 0.5em;">
<div id="projectname">Caffe2 - C++ API
</div>
<div id="projectbrief">A deep learning, cross platform ML framework</div>
</td>
</tr>
</tbody>
</table>
</div>
<!-- end header part -->
<!-- Generated by Doxygen 1.8.11 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
<div id="navrow1" class="tabs">
<ul class="tablist">
<li><a href="pages.html"><span>Related Pages</span></a></li>
<li><a href="modules.html"><span>Modules</span></a></li>
<li><a href="annotated.html"><span>Data Structures</span></a></li>
<li class="current"><a href="files.html"><span>Files</span></a></li>
<li><a href="/doxygen-c/html/classes.html"><span>C++ API</span></a></li>
<li><a href="/doxygen-python/html/annotated.html"><span>Python API</span></a></li>
<li><a href="https://github.com/caffe2/caffe2"><span>GitHub</span></a></li>
<li>
<div id="MSearchBox" class="MSearchBoxInactive">
<span class="left">
<img id="MSearchSelect" src="search/mag_sel.png"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
alt=""/>
<input type="text" id="MSearchField" value="Search" accesskey="S"
onfocus="searchBox.OnSearchFieldFocus(true)"
onblur="searchBox.OnSearchFieldFocus(false)"
onkeyup="searchBox.OnSearchFieldChange(event)"/>
</span><span class="right">
<a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
</span>
</div>
</li>
</ul>
</div>
<div id="navrow2" class="tabs2">
<ul class="tablist">
<li><a href="files.html"><span>File List</span></a></li>
<li><a href="globals.html"><span>Globals</span></a></li>
</ul>
</div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
</div>
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
<iframe src="javascript:void(0)" frameborder="0"
name="MSearchResults" id="MSearchResults">
</iframe>
</div>
<div id="nav-path" class="navpath">
<ul>
<li class="navelem"><a class="el" href="dir_20697b8f204bdfcab31e6b1a416f3ab8.html">caffe2</a></li><li class="navelem"><a class="el" href="dir_78eec69ac3a4b32ad49d9e5fc7146850.html">core</a></li> </ul>
</div>
</div><!-- top -->
<div class="header">
<div class="headertitle">
<div class="title">blob_serialization.h</div> </div>
</div><!--header-->
<div class="contents">
<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="preprocessor">#ifndef CAFFE2_CORE_BLOB_SERIALIZATION_H_</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="preprocessor">#define CAFFE2_CORE_BLOB_SERIALIZATION_H_</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> </div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="preprocessor">#include <limits></span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#include <future></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 <google/protobuf/repeated_field.h></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 "caffe2/core/blob.h"</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include "caffe2/core/blob_serializer_base.h"</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include "caffe2/core/tensor.h"</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include "caffe2/core/typeid.h"</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include "caffe2/core/types.h"</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include "caffe2/utils/simple_queue.h"</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> CAFFE2_DECLARE_int(caffe2_tensor_chunk_size);</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> CAFFE2_DECLARE_int(caffe2_max_tensor_serializer_threads);</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> CAFFE2_DECLARE_bool(caffe2_serialize_fp16_as_bytes);</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="keyword">namespace </span><a class="code" href="namespacecaffe2.html">caffe2</a> {</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> constexpr <span class="keyword">auto</span> kTensorBlobType = <span class="stringliteral">"Tensor"</span>;</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment">// String used to separate chunk id from the blob name when storing in DB</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> constexpr <span class="keyword">auto</span> kChunkIdSeparator = <span class="stringliteral">"#%"</span>;</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="comment">// The Blob serialization registry and serializer creator functions.</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> CAFFE_DECLARE_TYPED_REGISTRY(</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  BlobSerializerRegistry,</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  CaffeTypeId,</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  BlobSerializerBase,</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  std::unique_ptr);</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#define REGISTER_BLOB_SERIALIZER(id, ...) \</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor"> CAFFE_REGISTER_TYPED_CLASS(BlobSerializerRegistry, id, __VA_ARGS__)</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="comment">// Creates an operator with the given operator definition.</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">inline</span> unique_ptr<BlobSerializerBase> CreateSerializer(CaffeTypeId <span class="keywordtype">id</span>) {</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordflow">return</span> BlobSerializerRegistry()->Create(<span class="keywordtype">id</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"> 38</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="keyword">template</span> <<span class="keyword">class</span> Context></div><div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="classcaffe2_1_1_tensor_serializer.html"> 46</a></span> <span class="keyword">class </span><a class="code" href="classcaffe2_1_1_tensor_serializer.html">TensorSerializer</a> : <span class="keyword">public</span> <a class="code" href="classcaffe2_1_1_blob_serializer_base.html">BlobSerializerBase</a> {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <a class="code" href="classcaffe2_1_1_tensor_serializer.html">TensorSerializer</a>() : context_() {}</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  ~<a class="code" href="classcaffe2_1_1_tensor_serializer.html">TensorSerializer</a>()<span class="keyword"> override </span>{}</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordtype">void</span> <a class="code" href="classcaffe2_1_1_tensor_serializer.html#a5946d70063bdf3a892b94a65a2cf8fe1">Serialize</a>(</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keyword">const</span> <a class="code" href="classcaffe2_1_1_blob.html">Blob</a>& blob,</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keyword">const</span> <span class="keywordtype">string</span>& name,</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  SerializationAcceptor acceptor) <span class="keyword">override</span>;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordtype">void</span> SerializeWithChunkSize(</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keyword">const</span> <a class="code" href="classcaffe2_1_1_blob.html">Blob</a>& blob,</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">const</span> <span class="keywordtype">string</span>& name,</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  SerializationAcceptor acceptor,</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordtype">int</span> chunk_size) <span class="keyword">override</span>;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordtype">void</span> <a class="code" href="classcaffe2_1_1_tensor_serializer.html#a5946d70063bdf3a892b94a65a2cf8fe1">Serialize</a>(<span class="keyword">const</span> <a class="code" href="classcaffe2_1_1_tensor.html">Tensor<Context></a>& tensor, <span class="keyword">const</span> <span class="keywordtype">string</span>& name,</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  TensorProto* proto, <span class="keywordtype">size_t</span> chunkBegin, int32_t chunkSize);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="comment">// A utility function to store the device context detauls.</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordtype">void</span> StoreDeviceDetail(<span class="keyword">const</span> <a class="code" href="classcaffe2_1_1_tensor.html">Tensor<Context></a>& input, TensorProto* proto);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  Context context_;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> };</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00077"></a><span class="lineno"><a class="line" href="classcaffe2_1_1_blob_deserializer_base.html"> 77</a></span> <span class="keyword">class </span><a class="code" href="classcaffe2_1_1_blob_deserializer_base.html">BlobDeserializerBase</a> {</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keyword">virtual</span> ~<a class="code" href="classcaffe2_1_1_blob_deserializer_base.html">BlobDeserializerBase</a>() {}</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="comment">// Deserializes from a BlobProto object.</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> Deserialize(<span class="keyword">const</span> BlobProto& proto, <a class="code" href="classcaffe2_1_1_blob.html">Blob</a>* blob) = 0;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> };</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> CAFFE_DECLARE_REGISTRY(BlobDeserializerRegistry, <a class="code" href="classcaffe2_1_1_blob_deserializer_base.html">BlobDeserializerBase</a>);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="preprocessor">#define REGISTER_BLOB_DESERIALIZER(name, ...) \</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> <span class="preprocessor"> CAFFE_REGISTER_CLASS(BlobDeserializerRegistry, name, __VA_ARGS__)</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> <span class="comment">// Creates an operator with the given operator definition.</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> <span class="keyword">inline</span> unique_ptr<BlobDeserializerBase> CreateDeserializer(<span class="keyword">const</span> <span class="keywordtype">string</span>& type) {</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordflow">return</span> BlobDeserializerRegistry()->Create(type);</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> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <span class="keyword">template</span> <<span class="keyword">class</span> Context></div><div class="line"><a name="l00102"></a><span class="lineno"><a class="line" href="classcaffe2_1_1_tensor_deserializer.html"> 102</a></span> <span class="keyword">class </span><a class="code" href="classcaffe2_1_1_tensor_deserializer.html">TensorDeserializer</a> : <span class="keyword">public</span> <a class="code" href="classcaffe2_1_1_blob_deserializer_base.html">BlobDeserializerBase</a> {</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keywordtype">void</span> Deserialize(<span class="keyword">const</span> BlobProto& proto, <a class="code" href="classcaffe2_1_1_blob.html">Blob</a>* blob) <span class="keyword">override</span>;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordtype">void</span> Deserialize(<span class="keyword">const</span> TensorProto& proto, <a class="code" href="classcaffe2_1_1_tensor.html">Tensor<Context></a>* tensor);</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> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> <span class="comment">// Implementations</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="comment"></span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="keyword">namespace </span>detail {</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="keyword">template</span> <<span class="keyword">typename</span> SrcType, <span class="keyword">typename</span> DstType, <span class="keyword">class</span> Context></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> CopyToProtoAsIs(</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> size,</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keyword">const</span> SrcType* src,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  google::protobuf::RepeatedField<DstType>* field,</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  Context* context) {</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  static_assert(</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keyword">sizeof</span>(SrcType) == <span class="keyword">sizeof</span>(DstType),</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="stringliteral">"The source type and dest type cannot be copied as-is. Did "</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="stringliteral">"you mean CopyToProtoWithCast?"</span>);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  field->Reserve(size);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < size; ++i) {</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  field->Add(0);</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>  context->template Copy<SrcType, Context, CPUContext>(</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  size, src, <span class="keyword">reinterpret_cast<</span>SrcType*<span class="keyword">></span>(field->mutable_data()));</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// Make sure that we finish the copy into the protobuf.</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  context->FinishDeviceComputation();</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> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> <span class="keyword">template</span> <<span class="keyword">typename</span> SrcType, <span class="keyword">typename</span> DstType, <span class="keyword">class</span> Context></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> CopyToProtoWithCast(</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> size,</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keyword">const</span> SrcType* src,</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  google::protobuf::RepeatedField<DstType>* field,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  Context* context) {</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="comment">// TODO: we are having one unnecessary copy here if the context is already</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="comment">// CPUContext. Remove it if it is performance critical.</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  unique_ptr<SrcType[]> buffer(<span class="keyword">new</span> SrcType[size]);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  context->template Copy<SrcType, Context, CPUContext>(</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  size, src, buffer.get());</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  context->FinishDeviceComputation();</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  field->Reserve(size);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < size; ++i) {</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  field->Add(static_cast<DstType>(buffer[i]));</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> }</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="keyword">template</span> <<span class="keyword">typename</span> SrcType, <span class="keyword">typename</span> DstType, <span class="keyword">class</span> Context></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> CopyFromProtoAsIs(</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> size,</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keyword">const</span> google::protobuf::RepeatedField<SrcType>& field,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  DstType* dst,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  Context* context) {</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  static_assert(</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">sizeof</span>(SrcType) == <span class="keyword">sizeof</span>(DstType),</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="stringliteral">"The source type and dest type cannot be copied as-is. Did "</span></div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="stringliteral">"you mean CopyFromProtoWithCast?"</span>);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  CAFFE_ENFORCE_EQ(size, field.size(), <span class="stringliteral">"Incorrect proto field size."</span>);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  context->template Copy<DstType, CPUContext, Context>(</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  size, <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span>DstType*<span class="keyword">></span>(field.data()), dst);</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> </div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> <span class="keyword">template</span> <<span class="keyword">typename</span> SrcType, <span class="keyword">typename</span> DstType, <span class="keyword">class</span> Context></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> CopyFromProtoWithCast(</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> size,</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keyword">const</span> google::protobuf::RepeatedField<SrcType>& field,</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  DstType* dst,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  Context* context) {</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  CAFFE_ENFORCE_EQ(size, field.size(), <span class="stringliteral">"Incorrect proto field size."</span>);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="comment">// TODO: we are having one unnecessary copy here if the context is already</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="comment">// CPUContext. Remove it if it is performance critical.</span></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  unique_ptr<DstType[]> buffer(<span class="keyword">new</span> DstType[size]);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keyword">const</span> SrcType* src = field.data();</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < size; ++i) {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  buffer[i] = <span class="keyword">static_cast<</span>DstType<span class="keyword">></span>(src[i]);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  }</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  context->template Copy<DstType, CPUContext, Context>(size, buffer.get(), dst);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> }</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="comment">// namespace detail</span></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> <span class="keyword">template</span> <<span class="keyword">class</span> Context></div><div class="line"><a name="l00186"></a><span class="lineno"><a class="line" href="classcaffe2_1_1_tensor_serializer.html#a5946d70063bdf3a892b94a65a2cf8fe1"> 186</a></span> <span class="keywordtype">void</span> <a class="code" href="classcaffe2_1_1_tensor_serializer.html#a5946d70063bdf3a892b94a65a2cf8fe1">TensorSerializer<Context>::Serialize</a>(</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keyword">const</span> <a class="code" href="classcaffe2_1_1_blob.html">Blob</a>& blob,</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keyword">const</span> <span class="keywordtype">string</span>& name,</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  BlobSerializerBase::SerializationAcceptor acceptor) {</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  this->SerializeWithChunkSize(blob, name, acceptor, kDefaultChunkSize);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> }</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> </div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <span class="keyword">template</span> <<span class="keyword">class</span> Context></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="keywordtype">void</span> <a class="code" href="classcaffe2_1_1_tensor_serializer.html">TensorSerializer<Context>::SerializeWithChunkSize</a>(</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keyword">const</span> <a class="code" href="classcaffe2_1_1_blob.html">Blob</a>& blob,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keyword">const</span> <span class="keywordtype">string</span>& name,</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  BlobSerializerBase::SerializationAcceptor acceptor,</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordtype">int</span> chunk_size) {</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  CAFFE_ENFORCE(blob.<a class="code" href="classcaffe2_1_1_blob.html#a2200340329c81963cb0aa885f1af7958">IsType</a><<a class="code" href="classcaffe2_1_1_tensor.html">Tensor<Context></a>>());</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& tensor = blob.template Get<Tensor<Context>>();</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keywordflow">if</span> (chunk_size == kNoChunking) {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  chunk_size = tensor.size() + 1; <span class="comment">// to account for empty tensors</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (chunk_size == kDefaultChunkSize) {</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  chunk_size = FLAGS_caffe2_tensor_chunk_size;</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  }</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keyword">auto</span> processChunk = [&](int64_t chunkStart) {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  BlobProto blob_proto;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  blob_proto.set_name(name);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  blob_proto.set_type(kTensorBlobType);</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  TensorProto& proto = *blob_proto.mutable_tensor();</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  proto.set_name(name);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  this-><a class="code" href="classcaffe2_1_1_tensor_serializer.html#a5946d70063bdf3a892b94a65a2cf8fe1">Serialize</a>(</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  tensor, name, blob_proto.mutable_tensor(), chunkStart, chunk_size);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  acceptor(</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  MakeString(name, kChunkIdSeparator, chunkStart / chunk_size),</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  blob_proto.SerializeAsString());</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  };</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> </div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> <span class="preprocessor">#ifndef __ANDROID__</span></div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  std::vector<std::future<void>> futures;</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="comment">// Poorman's IOBound ThreadPool</span></div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <a class="code" href="classcaffe2_1_1_simple_queue.html">SimpleQueue<size_t></a> chunkQueue;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keyword">auto</span> task = [&]() {</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordtype">size_t</span> chunkStart;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordflow">while</span> (chunkQueue.Pop(&chunkStart)) {</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  processChunk(chunkStart);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  }</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>  <span class="keywordflow">if</span> (tensor.size() > chunk_size) {</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < FLAGS_caffe2_max_tensor_serializer_threads; ++i) {</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  futures.emplace_back(std::async(std::launch::async, task));</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>  }</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  VLOG(1) << <span class="stringliteral">"Serializing blob "</span> << name;</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="comment">// Serialize whole vector. If vector is empty, it's shape still needs to be</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="comment">// serialized in empty proto</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> chunkBegin = 0;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  chunkBegin < std::max(tensor.size(), <span class="keyword">static_cast<</span>TIndex<span class="keyword">></span>(1));</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  chunkBegin += chunk_size) {</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  VLOG(2) << <span class="stringliteral">"Starting a chunk at "</span> << chunkBegin;</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> <span class="preprocessor">#ifndef __ANDROID__</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordflow">if</span> (tensor.size() > chunk_size) {</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  chunkQueue.Push(chunkBegin);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="comment">// Sync mode for small tensors</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  processChunk(chunkBegin);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  }</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="comment">// Since Android does not have std::future, we will always do sync mode</span></div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  processChunk(chunkBegin);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  }</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> </div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> <span class="preprocessor">#ifndef __ANDROID__</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  chunkQueue.NoMoreJobs();</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& fut : futures) {</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  fut.get();</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="preprocessor">#endif</span></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> </div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> <span class="keyword">template</span> <<span class="keyword">class</span> Context></div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <span class="keywordtype">void</span> <a class="code" href="classcaffe2_1_1_tensor_serializer.html#a5946d70063bdf3a892b94a65a2cf8fe1">TensorSerializer<Context>::Serialize</a>(</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keyword">const</span> <a class="code" href="classcaffe2_1_1_tensor.html">Tensor<Context></a>& input,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keyword">const</span> <span class="keywordtype">string</span>& <span class="comment">/*name*/</span>,</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  TensorProto* proto_ptr,</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keywordtype">size_t</span> chunkBegin,</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  int32_t chunkSize) {</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  CAFFE_ENFORCE(</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  chunkBegin <= input.<a class="code" href="classcaffe2_1_1_tensor.html#a87087b9548e9bad215d663389abda32e">size</a>(),</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="stringliteral">"Chunk begin is out of tensor: "</span>,</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  chunkBegin,</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="charliteral">' '</span>,</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  input.<a class="code" href="classcaffe2_1_1_tensor.html#a87087b9548e9bad215d663389abda32e">size</a>());</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keywordflow">if</span> (chunkBegin + chunkSize > input.<a class="code" href="classcaffe2_1_1_tensor.html#a87087b9548e9bad215d663389abda32e">size</a>()) {</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  chunkSize = input.<a class="code" href="classcaffe2_1_1_tensor.html#a87087b9548e9bad215d663389abda32e">size</a>() - chunkBegin;</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> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  CAFFE_ENFORCE(</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  input.<a class="code" href="classcaffe2_1_1_tensor.html#a1a62756c55baf42bfa933cd998398de7">raw_data</a>() || chunkSize == 0,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="stringliteral">"The input does not have data input yet. This is probably because you "</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="stringliteral">"created a tensor of non-zero shape but never filled its data via "</span></div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="stringliteral">"mutable_data() calls. This means that it makes no sense to serialize "</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="stringliteral">"the tensor content."</span>);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> </div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  TensorProto& proto = *proto_ptr;</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  proto.mutable_segment()->set_begin(chunkBegin);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  proto.mutable_segment()->set_end(chunkBegin + chunkSize);</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < input.<a class="code" href="classcaffe2_1_1_tensor.html#aea51c872873f4db0abad47713315e81f">ndim</a>(); ++i) {</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  proto.add_dims(input.<a class="code" href="classcaffe2_1_1_tensor.html#abec0a0587f4afb6baf486bb0659ec47d">dim</a>(i));</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  }</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="keyword">const</span> TensorProto::DataType data_type = TypeMetaToDataType(input.<a class="code" href="classcaffe2_1_1_tensor.html#a0503013b9587314bb88b060dd73f678e">meta</a>());</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  proto.set_data_type(data_type);</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  StoreDeviceDetail(input, &proto);</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="comment">// A lot of copypaste is error prone. Should we create a macro for this?</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keywordflow">switch</span> (data_type) {</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keywordflow">case</span> TensorProto_DataType_FLOAT:</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  detail::CopyToProtoAsIs(</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  chunkSize,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  input.template data<float>() + chunkBegin,</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  proto.mutable_float_data(),</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  &this->context_);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keywordflow">case</span> TensorProto_DataType_INT32:</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  detail::CopyToProtoAsIs(</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  chunkSize,</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  input.template data<int>() + chunkBegin,</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  proto.mutable_int32_data(),</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  &this->context_);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="keywordflow">case</span> TensorProto_DataType_BYTE:</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  LOG(FATAL) << <span class="stringliteral">"This should not happen. When serializing, "</span></div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="stringliteral">"BYTE is deprecated and moved to UINT8."</span>;</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keywordflow">case</span> TensorProto_DataType_STRING:</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>  proto.mutable_string_data()->Reserve(chunkSize);</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="keyword">const</span> <span class="keywordtype">string</span>* content = input.template data<string>();</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = chunkBegin; i < chunkBegin + chunkSize; ++i) {</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  proto.add_string_data(content[i]);</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>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  }</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="keywordflow">case</span> TensorProto_DataType_BOOL:</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  detail::CopyToProtoWithCast(</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  chunkSize,</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  input.template data<bool>() + chunkBegin,</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  proto.mutable_int32_data(),</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  &this->context_);</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keywordflow">case</span> TensorProto_DataType_UINT8:</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  detail::CopyToProtoWithCast(</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  chunkSize,</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  input.template data<uint8_t>() + chunkBegin,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  proto.mutable_int32_data(),</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  &this->context_);</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keywordflow">case</span> TensorProto_DataType_INT8:</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  detail::CopyToProtoWithCast(</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  chunkSize,</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  input.template data<int8_t>() + chunkBegin,</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  proto.mutable_int32_data(),</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  &this->context_);</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keywordflow">case</span> TensorProto_DataType_UINT16:</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  detail::CopyToProtoWithCast(</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  chunkSize,</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  input.template data<uint16_t>() + chunkBegin,</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  proto.mutable_int32_data(),</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  &this->context_);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keywordflow">case</span> TensorProto_DataType_INT16:</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  detail::CopyToProtoWithCast(</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  chunkSize,</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  input.template data<int16_t>() + chunkBegin,</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  proto.mutable_int32_data(),</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  &this->context_);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="keywordflow">case</span> TensorProto_DataType_INT64:</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  detail::CopyToProtoAsIs(</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  chunkSize,</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  input.template data<int64_t>() + chunkBegin,</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  proto.mutable_int64_data(),</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  &this->context_);</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keywordflow">case</span> TensorProto_DataType_FLOAT16: {</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keywordflow">if</span> (FLAGS_caffe2_serialize_fp16_as_bytes) {</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> kValue = 1;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  CAFFE_ENFORCE_EQ(</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  reinterpret_cast<const char*>(&kValue)[0],</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  1,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="stringliteral">"Serialization of FLOAT16 on big endian platform "</span></div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="stringliteral">"is not written yet."</span>);</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  unique_ptr<char[]> buffer(<span class="keyword">new</span> <span class="keywordtype">char</span>[2 * chunkSize]);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  this->context_.template Copy<char, Context, CPUContext>(</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  2 * chunkSize,</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span><span class="keywordtype">char</span>*<span class="keyword">></span>(</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  input.template data<float16>() + chunkBegin),</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  buffer.get());</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  this->context_.FinishDeviceComputation();</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  proto.set_byte_data(buffer.release(), 2 * chunkSize);</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  detail::CopyToProtoWithCast(</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  chunkSize,</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  reinterpret_cast<const uint16_t*>(input.template data<float16>()) +</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  chunkBegin,</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  proto.mutable_int32_data(),</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  &this->context_);</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  }</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  } <span class="keywordflow">break</span>;</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keywordflow">case</span> TensorProto_DataType_DOUBLE:</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  detail::CopyToProtoAsIs(</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  chunkSize,</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  input.template data<double>() + chunkBegin,</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  proto.mutable_double_data(),</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  &this->context_);</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keywordflow">case</span> TensorProto_DataType_UNDEFINED: {</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  proto.mutable_string_data()->Reserve(chunkSize);</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <a class="code" href="classcaffe2_1_1_blob.html">Blob</a> temp_blob;</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* raw_data = <span class="keyword">static_cast<</span><span class="keyword">const </span><span class="keywordtype">char</span>*<span class="keyword">></span>(input.<a class="code" href="classcaffe2_1_1_tensor.html#a1a62756c55baf42bfa933cd998398de7">raw_data</a>());</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = chunkBegin; i < chunkBegin + chunkSize; ++i) {</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  temp_blob.<a class="code" href="classcaffe2_1_1_blob.html#a4e110fabb1ecbe684576dc7b20ea6516">ShareExternal</a>(</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  const_cast<char*>(raw_data + i * input.<a class="code" href="classcaffe2_1_1_tensor.html#a11e2525d6bea3b7d098982e2f23699ca">itemsize</a>()), input.<a class="code" href="classcaffe2_1_1_tensor.html#a0503013b9587314bb88b060dd73f678e">meta</a>());</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  proto.add_string_data(temp_blob.<a class="code" href="classcaffe2_1_1_blob.html#aa77a7a69a0321d80895142e51dd287d5">Serialize</a>(<span class="stringliteral">""</span>));</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  }</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  } <span class="keywordflow">break</span>;</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="comment">// Note: we intentially do not provide "default:" so if any new data types</span></div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="comment">// are added, the compiler should warn the user to add the case here.</span></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> }</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="keyword">template</span> <<span class="keyword">class</span> Context></div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> <span class="keywordtype">void</span> <a class="code" href="classcaffe2_1_1_tensor_deserializer.html">TensorDeserializer<Context>::Deserialize</a>(</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keyword">const</span> BlobProto& blob_proto,</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <a class="code" href="classcaffe2_1_1_blob.html">Blob</a>* blob) {</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  Deserialize(blob_proto.tensor(), blob-><a class="code" href="classcaffe2_1_1_blob.html#a355cff5bfcdfce83ac53ce2a2eef9ee4">GetMutable</a><<a class="code" href="classcaffe2_1_1_tensor.html">Tensor<Context></a>>());</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> </div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> <span class="keyword">template</span> <<span class="keyword">class</span> Context></div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span> <span class="keywordtype">void</span> <a class="code" href="classcaffe2_1_1_tensor_deserializer.html">TensorDeserializer<Context>::Deserialize</a>(</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keyword">const</span> TensorProto& proto,</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <a class="code" href="classcaffe2_1_1_tensor.html">Tensor<Context></a>* tensor) {</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="comment">// We create a local context for deserializing. Since Caffe2 contexts are</span></div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="comment">// usually lightweighted, this should not involve too much overhead.</span></div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  Context context(proto.device_detail());</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  context.SwitchToDevice(0);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  vector<TIndex> dims;</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> TIndex d : proto.dims()) {</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  dims.push_back(d);</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  }</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  tensor-><a class="code" href="classcaffe2_1_1_tensor.html#a359b5ed5cfd9beaf7f62a5561d939c3b">Resize</a>(dims);</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> </div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  int64_t chunkBegin = 0;</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="keyword">auto</span> chunkEnd = tensor-><a class="code" href="classcaffe2_1_1_tensor.html#a87087b9548e9bad215d663389abda32e">size</a>();</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keywordflow">if</span> (proto.has_segment()) {</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  chunkBegin = proto.segment().begin();</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  chunkEnd = proto.segment().end();</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>  CAFFE_ENFORCE(</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  0 <= chunkBegin && chunkBegin <= chunkEnd && chunkEnd <= tensor->size(),</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="stringliteral">"Invalid chunk "</span>,</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  chunkBegin,</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="charliteral">' '</span>,</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  chunkEnd,</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="stringliteral">" with total tensor size "</span>,</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  tensor-><a class="code" href="classcaffe2_1_1_tensor.html#a87087b9548e9bad215d663389abda32e">size</a>());</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="keyword">auto</span> chunkSize = chunkEnd - chunkBegin;</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">switch</span> (proto.data_type()) {</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keywordflow">case</span> TensorProto_DataType_FLOAT:</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  detail::CopyFromProtoAsIs(</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  chunkSize,</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  proto.float_data(),</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  tensor->template mutable_data<float>() + chunkBegin,</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  &context);</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keywordflow">case</span> TensorProto_DataType_INT32:</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  detail::CopyFromProtoAsIs(</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  chunkSize,</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  proto.int32_data(),</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  tensor->template mutable_data<int>() + chunkBegin,</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  &context);</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keywordflow">case</span> TensorProto_DataType_BYTE:</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="comment">// Since BYTE stores the data in a string field instead of a repreated</span></div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="comment">// field we will have it special cased.</span></div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  CAFFE_ENFORCE_EQ(</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  chunkSize, proto.byte_data().size(), <span class="stringliteral">"Incorrect proto field size."</span>);</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  context.template Copy<uint8_t, Context, CPUContext>(</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  chunkSize,</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span>uint8_t*<span class="keyword">></span>(proto.byte_data().data()),</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  tensor->template mutable_data<uint8_t>() + chunkBegin);</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="keywordflow">case</span> TensorProto_DataType_STRING:</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="comment">// Special handing of string because it is a non-fundamental type.</span></div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  {</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="keywordtype">string</span>* content = tensor->template mutable_data<string>();</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < chunkSize; ++i) {</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  content[i + chunkBegin] = proto.string_data(i);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  }</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  }</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keywordflow">case</span> TensorProto_DataType_BOOL:</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  detail::CopyFromProtoWithCast(</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  chunkSize,</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  proto.int32_data(),</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  tensor->template mutable_data<bool>() + chunkBegin,</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  &context);</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="keywordflow">case</span> TensorProto_DataType_UINT8:</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  detail::CopyFromProtoWithCast(</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  chunkSize,</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  proto.int32_data(),</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  tensor->template mutable_data<uint8_t>() + chunkBegin,</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  &context);</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="keywordflow">case</span> TensorProto_DataType_INT8:</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  detail::CopyFromProtoWithCast(</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  chunkSize,</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  proto.int32_data(),</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  tensor->template mutable_data<int8_t>() + chunkBegin,</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  &context);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <span class="keywordflow">case</span> TensorProto_DataType_UINT16:</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  detail::CopyFromProtoWithCast(</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  chunkSize,</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  proto.int32_data(),</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  tensor->template mutable_data<uint16_t>() + chunkBegin,</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  &context);</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <span class="keywordflow">case</span> TensorProto_DataType_INT16:</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  detail::CopyFromProtoWithCast(</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  chunkSize,</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  proto.int32_data(),</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  tensor->template mutable_data<int16_t>() + chunkBegin,</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  &context);</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  <span class="keywordflow">case</span> TensorProto_DataType_INT64:</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  detail::CopyFromProtoAsIs(</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  chunkSize,</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  proto.int64_data(),</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  tensor->template mutable_data<int64_t>() + chunkBegin,</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  &context);</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keywordflow">case</span> TensorProto_DataType_FLOAT16:</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="keywordflow">if</span> (proto.has_byte_data()) {</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> kValue = 1;</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  CAFFE_ENFORCE_EQ(</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  reinterpret_cast<const char*>(&kValue)[0],</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  1,</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="stringliteral">"Serialization of FLOAT16 on big endian platform "</span></div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <span class="stringliteral">"is not written yet."</span>);</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  CAFFE_ENFORCE_EQ(</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  2 * chunkSize,</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  proto.byte_data().size(),</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="stringliteral">"Incorrect proto field size."</span>);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  context.template Copy<float16, Context, CPUContext>(</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  chunkSize,</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span>float16*<span class="keyword">></span>(proto.byte_data().data()),</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  tensor->template mutable_data<float16>() + chunkBegin);</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <span class="comment">// Backward compatibility with models which used int32_data field</span></div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  detail::CopyFromProtoWithCast(</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  chunkSize,</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  proto.int32_data(),</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keyword">reinterpret_cast<</span>uint16_t*<span class="keyword">></span>(</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  tensor->template mutable_data<float16>()) +</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  chunkBegin,</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  &context);</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  }</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="keywordflow">case</span> TensorProto_DataType_DOUBLE:</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  detail::CopyFromProtoAsIs(</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  chunkSize,</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  proto.double_data(),</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  tensor->template mutable_data<double>() + chunkBegin,</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  &context);</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="keywordflow">case</span> TensorProto_DataType_UNDEFINED: {</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <a class="code" href="classcaffe2_1_1_blob.html">Blob</a> temp_blob;</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="keywordtype">void</span>* raw_ptr = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < chunkSize; ++i) {</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  temp_blob.<a class="code" href="classcaffe2_1_1_blob.html#a77fd09388bb320062d332394be9b80cf">Deserialize</a>(proto.string_data(i));</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <span class="keywordflow">if</span> (i == 0) {</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  raw_ptr = tensor-><a class="code" href="classcaffe2_1_1_tensor.html#a83bade6123fa388bf59497f2bad998d4">raw_mutable_data</a>(temp_blob.<a class="code" href="classcaffe2_1_1_blob.html#a50308d6febe9e777f19413994a9774a5">meta</a>());</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  }</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  temp_blob.<a class="code" href="classcaffe2_1_1_blob.html#a50308d6febe9e777f19413994a9774a5">meta</a>().<a class="code" href="classcaffe2_1_1_type_meta.html#ac8dd4e4823774c12643659bc4acf9872">copy</a>()(</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  temp_blob.GetRaw(),</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <span class="keyword">static_cast<</span><span class="keywordtype">char</span>*<span class="keyword">></span>(raw_ptr) +</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  (i + chunkBegin) * temp_blob.<a class="code" href="classcaffe2_1_1_blob.html#a50308d6febe9e777f19413994a9774a5">meta</a>().<a class="code" href="classcaffe2_1_1_type_meta.html#afadf4203c5a73569932fb9a0827408c8">itemsize</a>(),</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  1);</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"> 580</span>  }</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  context.FinishDeviceComputation();</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> } <span class="comment">// namespace caffe2</span></div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> </div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span> <span class="preprocessor">#endif // CAFFE2_CORE_BLOB_SERIALIZATION_H_</span></div><div class="ttc" id="classcaffe2_1_1_blob_html"><div class="ttname"><a href="classcaffe2_1_1_blob.html">caffe2::Blob</a></div><div class="ttdoc">Blob is a general container that hosts a typed pointer. </div><div class="ttdef"><b>Definition:</b> <a href="blob_8h_source.html#l00025">blob.h:25</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_html_a11e2525d6bea3b7d098982e2f23699ca"><div class="ttname"><a href="classcaffe2_1_1_tensor.html#a11e2525d6bea3b7d098982e2f23699ca">caffe2::Tensor::itemsize</a></div><div class="ttdeci">size_t itemsize() const </div><div class="ttdoc">Return the number of bytes each item takes in the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="tensor_8h_source.html#l00597">tensor.h:597</a></div></div>
<div class="ttc" id="classcaffe2_1_1_blob_html_a4e110fabb1ecbe684576dc7b20ea6516"><div class="ttname"><a href="classcaffe2_1_1_blob.html#a4e110fabb1ecbe684576dc7b20ea6516">caffe2::Blob::ShareExternal</a></div><div class="ttdeci">std::remove_const< T >::type * ShareExternal(typename std::remove_const< T >::type *allocated)</div><div class="ttdoc">Sets the underlying object to the allocated one, but does not take over the ownership of the passed i...</div><div class="ttdef"><b>Definition:</b> <a href="blob_8h_source.html#l00163">blob.h:163</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_html_a0503013b9587314bb88b060dd73f678e"><div class="ttname"><a href="classcaffe2_1_1_tensor.html#a0503013b9587314bb88b060dd73f678e">caffe2::Tensor::meta</a></div><div class="ttdeci">const TypeMeta & meta() const </div><div class="ttdoc">Returns the TypeMeta object associated with the current data type. </div><div class="ttdef"><b>Definition:</b> <a href="tensor_8h_source.html#l00648">tensor.h:648</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_html_abec0a0587f4afb6baf486bb0659ec47d"><div class="ttname"><a href="classcaffe2_1_1_tensor.html#abec0a0587f4afb6baf486bb0659ec47d">caffe2::Tensor::dim</a></div><div class="ttdeci">TIndex dim(const int i) const </div><div class="ttdoc">Returns the i-th dimension of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="tensor_8h_source.html#l00671">tensor.h:671</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_serializer_html"><div class="ttname"><a href="classcaffe2_1_1_tensor_serializer.html">caffe2::TensorSerializer</a></div><div class="ttdoc">TensorSerializer is the serializer for Tensors. </div><div class="ttdef"><b>Definition:</b> <a href="blob__serialization_8h_source.html#l00046">blob_serialization.h:46</a></div></div>
<div class="ttc" id="classcaffe2_1_1_blob_deserializer_base_html"><div class="ttname"><a href="classcaffe2_1_1_blob_deserializer_base.html">caffe2::BlobDeserializerBase</a></div><div class="ttdoc">BlobDeserializerBase is an abstract class that deserializes a blob from a BlobProto or a TensorProto...</div><div class="ttdef"><b>Definition:</b> <a href="blob__serialization_8h_source.html#l00077">blob_serialization.h:77</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_html"><div class="ttname"><a href="classcaffe2_1_1_tensor.html">caffe2::Tensor</a></div><div class="ttdoc">Tensor is the basic class in Caffe2 that stores a contiguous memory with its shape information...</div><div class="ttdef"><b>Definition:</b> <a href="tensor_8h_source.html#l00093">tensor.h:93</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_html_a87087b9548e9bad215d663389abda32e"><div class="ttname"><a href="classcaffe2_1_1_tensor.html#a87087b9548e9bad215d663389abda32e">caffe2::Tensor::size</a></div><div class="ttdeci">TIndex size() const </div><div class="ttdoc">Returns the size (i.e. </div><div class="ttdef"><b>Definition:</b> <a href="tensor_8h_source.html#l00593">tensor.h:593</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_serializer_html_a5946d70063bdf3a892b94a65a2cf8fe1"><div class="ttname"><a href="classcaffe2_1_1_tensor_serializer.html#a5946d70063bdf3a892b94a65a2cf8fe1">caffe2::TensorSerializer::Serialize</a></div><div class="ttdeci">void Serialize(const Blob &blob, const string &name, SerializationAcceptor acceptor) override</div><div class="ttdoc">Serializes a Blob. </div><div class="ttdef"><b>Definition:</b> <a href="blob__serialization_8h_source.html#l00186">blob_serialization.h:186</a></div></div>
<div class="ttc" id="classcaffe2_1_1_blob_html_aa77a7a69a0321d80895142e51dd287d5"><div class="ttname"><a href="classcaffe2_1_1_blob.html#aa77a7a69a0321d80895142e51dd287d5">caffe2::Blob::Serialize</a></div><div class="ttdeci">void Serialize(const string &name, BlobSerializerBase::SerializationAcceptor acceptor, int chunk_size=kDefaultChunkSize) const </div><div class="ttdoc">Serializes the current blob, if possible. </div><div class="ttdef"><b>Definition:</b> <a href="blob__serialization_8cc_source.html#l00065">blob_serialization.cc:65</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_html_a359b5ed5cfd9beaf7f62a5561d939c3b"><div class="ttname"><a href="classcaffe2_1_1_tensor.html#a359b5ed5cfd9beaf7f62a5561d939c3b">caffe2::Tensor::Resize</a></div><div class="ttdeci">void Resize(Ts...dim_source)</div><div class="ttdoc">Resizes a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="tensor_8h_source.html#l00288">tensor.h:288</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_deserializer_html"><div class="ttname"><a href="classcaffe2_1_1_tensor_deserializer.html">caffe2::TensorDeserializer</a></div><div class="ttdoc">TensorDeserializer is the deserializer for Tensors. </div><div class="ttdef"><b>Definition:</b> <a href="blob__serialization_8h_source.html#l00102">blob_serialization.h:102</a></div></div>
<div class="ttc" id="namespacecaffe2_html"><div class="ttname"><a href="namespacecaffe2.html">caffe2</a></div><div class="ttdoc">A global dictionary that holds information about what Caffe2 modules have been loaded in the current ...</div><div class="ttdef"><b>Definition:</b> <a href="convert__encoded__to__raw__leveldb_8cc_source.html#l00047">convert_encoded_to_raw_leveldb.cc:47</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_html_a1a62756c55baf42bfa933cd998398de7"><div class="ttname"><a href="classcaffe2_1_1_tensor.html#a1a62756c55baf42bfa933cd998398de7">caffe2::Tensor::raw_data</a></div><div class="ttdeci">const void * raw_data() const </div><div class="ttdoc">Returns a const raw void* pointer of the underlying storage. </div><div class="ttdef"><b>Definition:</b> <a href="tensor_8h_source.html#l00472">tensor.h:472</a></div></div>
<div class="ttc" id="classcaffe2_1_1_type_meta_html_ac8dd4e4823774c12643659bc4acf9872"><div class="ttname"><a href="classcaffe2_1_1_type_meta.html#ac8dd4e4823774c12643659bc4acf9872">caffe2::TypeMeta::copy</a></div><div class="ttdeci">TypedCopy copy() const </div><div class="ttdoc">Returns the typed copy function pointer for individual iterms. </div><div class="ttdef"><b>Definition:</b> <a href="typeid_8h_source.html#l00155">typeid.h:155</a></div></div>
<div class="ttc" id="classcaffe2_1_1_blob_html_a355cff5bfcdfce83ac53ce2a2eef9ee4"><div class="ttname"><a href="classcaffe2_1_1_blob.html#a355cff5bfcdfce83ac53ce2a2eef9ee4">caffe2::Blob::GetMutable</a></div><div class="ttdeci">T * GetMutable(bool *is_new_object=nullptr)</div><div class="ttdoc">Gets a mutable pointer to the stored object. </div><div class="ttdef"><b>Definition:</b> <a href="blob_8h_source.html#l00101">blob.h:101</a></div></div>
<div class="ttc" id="classcaffe2_1_1_blob_html_a50308d6febe9e777f19413994a9774a5"><div class="ttname"><a href="classcaffe2_1_1_blob.html#a50308d6febe9e777f19413994a9774a5">caffe2::Blob::meta</a></div><div class="ttdeci">const TypeMeta & meta() const </div><div class="ttdoc">Returns the meta info of the blob. </div><div class="ttdef"><b>Definition:</b> <a href="blob_8h_source.html#l00063">blob.h:63</a></div></div>
<div class="ttc" id="classcaffe2_1_1_blob_html_a77fd09388bb320062d332394be9b80cf"><div class="ttname"><a href="classcaffe2_1_1_blob.html#a77fd09388bb320062d332394be9b80cf">caffe2::Blob::Deserialize</a></div><div class="ttdeci">void Deserialize(const string &content)</div><div class="ttdoc">Deserializes from a string containing either BlobProto or TensorProto. </div><div class="ttdef"><b>Definition:</b> <a href="blob__serialization_8cc_source.html#l00101">blob_serialization.cc:101</a></div></div>
<div class="ttc" id="classcaffe2_1_1_blob_html_a2200340329c81963cb0aa885f1af7958"><div class="ttname"><a href="classcaffe2_1_1_blob.html#a2200340329c81963cb0aa885f1af7958">caffe2::Blob::IsType</a></div><div class="ttdeci">bool IsType() const </div><div class="ttdoc">Checks if the content stored in the blob is of type T. </div><div class="ttdef"><b>Definition:</b> <a href="blob_8h_source.html#l00058">blob.h:58</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_html_aea51c872873f4db0abad47713315e81f"><div class="ttname"><a href="classcaffe2_1_1_tensor.html#aea51c872873f4db0abad47713315e81f">caffe2::Tensor::ndim</a></div><div class="ttdeci">int ndim() const </div><div class="ttdoc">Returns the number of dimensions of the data. </div><div class="ttdef"><b>Definition:</b> <a href="tensor_8h_source.html#l00589">tensor.h:589</a></div></div>
<div class="ttc" id="classcaffe2_1_1_simple_queue_html"><div class="ttname"><a href="classcaffe2_1_1_simple_queue.html">caffe2::SimpleQueue</a></div><div class="ttdef"><b>Definition:</b> <a href="simple__queue_8h_source.html#l00022">simple_queue.h:22</a></div></div>
<div class="ttc" id="classcaffe2_1_1_tensor_html_a83bade6123fa388bf59497f2bad998d4"><div class="ttname"><a href="classcaffe2_1_1_tensor.html#a83bade6123fa388bf59497f2bad998d4">caffe2::Tensor::raw_mutable_data</a></div><div class="ttdeci">void * raw_mutable_data(const TypeMeta &meta)</div><div class="ttdoc">Returns a mutable raw pointer of the underlying storage. </div><div class="ttdef"><b>Definition:</b> <a href="tensor_8h_source.html#l00510">tensor.h:510</a></div></div>
<div class="ttc" id="classcaffe2_1_1_type_meta_html_afadf4203c5a73569932fb9a0827408c8"><div class="ttname"><a href="classcaffe2_1_1_type_meta.html#afadf4203c5a73569932fb9a0827408c8">caffe2::TypeMeta::itemsize</a></div><div class="ttdeci">const size_t & itemsize() const </div><div class="ttdoc">Returns the size of the item. </div><div class="ttdef"><b>Definition:</b> <a href="typeid_8h_source.html#l00143">typeid.h:143</a></div></div>
<div class="ttc" id="classcaffe2_1_1_blob_serializer_base_html"><div class="ttname"><a href="classcaffe2_1_1_blob_serializer_base.html">caffe2::BlobSerializerBase</a></div><div class="ttdoc">BlobSerializerBase is an abstract class that serializes a blob to a string. </div><div class="ttdef"><b>Definition:</b> <a href="blob__serializer__base_8h_source.html#l00023">blob_serializer_base.h:23</a></div></div>
</div><!-- fragment --></div><!-- contents -->
<!-- HTML footer for doxygen 1.8.14-->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
Generated on Thu Apr 19 2018 13:03:49 for Caffe2 - C++ API by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
</a> 1.8.11
</small></address>
<div class="footerContainer">
<div id="footer_wrap" class="wrapper footerWrapper">
<div class="footerBlocks">
<div id="fb_oss" class="footerSection fbOpenSourceFooter">
<svg class="facebookOSSLogoSvg" viewBox="0 0 1133.9 1133.9" x="0px" y="0px" height=50 width=50>
<g>
<path class="logoRing outerRing" d="M 498.3 3.7 c 153.6 88.9 307.3 177.7 461.1 266.2 c 7.6 4.4 10.3 9.1 10.3 17.8 c -0.3 179.1 -0.2 358.3 0 537.4 c 0 8.1 -2.4 12.8 -9.7 17.1 c -154.5 88.9 -308.8 178.1 -462.9 267.5 c -9 5.2 -15.5 5.3 -24.6 0.1 c -153.9 -89.2 -307.9 -178 -462.1 -266.8 C 3 838.8 0 833.9 0 825.1 c 0.3 -179.1 0.2 -358.3 0 -537.4 c 0 -8.6 2.6 -13.6 10.2 -18 C 164.4 180.9 318.4 92 472.4 3 C 477 -1.5 494.3 -0.7 498.3 3.7 Z M 48.8 555.3 c 0 79.9 0.2 159.9 -0.2 239.8 c -0.1 10 3 15.6 11.7 20.6 c 137.2 78.8 274.2 157.8 411 237.3 c 9.9 5.7 17 5.7 26.8 0.1 c 137.5 -79.8 275.2 -159.2 412.9 -238.5 c 7.4 -4.3 10.5 -8.9 10.5 -17.8 c -0.3 -160.2 -0.3 -320.5 0 -480.7 c 0 -8.8 -2.8 -13.6 -10.3 -18 C 772.1 218 633.1 137.8 494.2 57.4 c -6.5 -3.8 -11.5 -4.5 -18.5 -0.5 C 336.8 137.4 197.9 217.7 58.8 297.7 c -7.7 4.4 -10.2 9.2 -10.2 17.9 C 48.9 395.5 48.8 475.4 48.8 555.3 Z" />
<path class="logoRing middleRing" d="M 184.4 555.9 c 0 -33.3 -1 -66.7 0.3 -100 c 1.9 -48 24.1 -86 64.7 -110.9 c 54.8 -33.6 110.7 -65.5 167 -96.6 c 45.7 -25.2 92.9 -24.7 138.6 1 c 54.4 30.6 108.7 61.5 162.2 93.7 c 44 26.5 67.3 66.8 68 118.4 c 0.9 63.2 0.9 126.5 0 189.7 c -0.7 50.6 -23.4 90.7 -66.6 116.9 c -55 33.4 -110.8 65.4 -167.1 96.5 c -43.4 24 -89 24.2 -132.3 0.5 c -57.5 -31.3 -114.2 -64 -170 -98.3 c -41 -25.1 -62.9 -63.7 -64.5 -112.2 C 183.5 621.9 184.3 588.9 184.4 555.9 Z M 232.9 556.3 c 0 29.5 0.5 59.1 -0.1 88.6 c -0.8 39.2 16.9 67.1 50.2 86.2 c 51.2 29.4 102.2 59.2 153.4 88.4 c 31.4 17.9 63.6 18.3 95 0.6 c 53.7 -30.3 107.1 -61.2 160.3 -92.5 c 29.7 -17.5 45 -44.5 45.3 -78.8 c 0.6 -61.7 0.5 -123.5 0 -185.2 c -0.3 -34.4 -15.3 -61.5 -44.9 -79 C 637.7 352.6 583 320.8 527.9 290 c -27.5 -15.4 -57.2 -16.1 -84.7 -0.7 c -56.9 31.6 -113.4 64 -169.1 97.6 c -26.4 15.9 -40.7 41.3 -41.1 72.9 C 232.6 491.9 232.9 524.1 232.9 556.3 Z" />
<path class="logoRing innerRing" d="M 484.9 424.4 c 69.8 -2.8 133.2 57.8 132.6 132 C 617 630 558.5 688.7 484.9 689.1 c -75.1 0.4 -132.6 -63.6 -132.7 -132.7 C 352.1 485 413.4 421.5 484.9 424.4 Z M 401.3 556.7 c -3.4 37.2 30.5 83.6 83 84.1 c 46.6 0.4 84.8 -37.6 84.9 -84 c 0.1 -46.6 -37.2 -84.4 -84.2 -84.6 C 432.2 472.1 397.9 518.3 401.3 556.7 Z" />
</g>
</svg>
<h2>Facebook Open Source</h2>
</div>
<div class="footerSection">
<a class="footerLink" href="https://code.facebook.com/projects/" target="_blank">Open Source Projects</a>
<a class="footerLink" href="https://github.com/facebook/" target="_blank">GitHub</a>
<a class="footerLink" href="https://twitter.com/fbOpenSource" target="_blank">Twitter</a>
</div>
<div class="footerSection rightAlign">
<a class="footerLink" href="https://github.com/caffe2/caffe2" target="_blank">Contribute to this project on GitHub</a>
</div>
</div>
</div>
</div>
<script type="text/javascript" src="/js/jekyll-link-anchors.js"></script>
<script>
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','//www.google-analytics.com/analytics.js','ga');
ga('create', '{{ site.gacode }}', 'auto');
ga('send', 'pageview');
</script>
</body>
</html>