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<div class="title">percentile_op.cc</div> </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="preprocessor">#include "caffe2/operators/percentile_op.h"</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> </div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="keyword">namespace </span><a class="code" href="namespacecaffe2.html">caffe2</a> {</div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> </div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="keyword">template</span> <></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="keywordtype">bool</span> PercentileOp<CPUContext>::RunOnDevice() {</div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& original_values = Input(X);</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>  CAFFE_ENFORCE_EQ(original_values.ndim(), 2);</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>  <span class="keyword">const</span> <span class="keyword">auto</span> num_examples = original_values.dim(0);</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>* original_values_data = original_values.template data<float>();</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>  <span class="keyword">const</span> <span class="keyword">auto</span> num_features = original_values.dim(1);</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& value_pct_pairs = Input(VAL_PCT_PAIRS);</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  CAFFE_ENFORCE_EQ(value_pct_pairs.ndim(), 2);</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  CAFFE_ENFORCE_EQ(value_pct_pairs.dim(1), 2);</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_values = value_pct_pairs.dim(0);</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>* value_pct_data = value_pct_pairs.template data<float>();</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& lengths = Input(LENS);</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* lengths_data = lengths.template data<int>();</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  CAFFE_ENFORCE_EQ(lengths.size(), num_features);</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  CAFFE_ENFORCE_EQ(</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  std::accumulate(lengths_data, lengths_data + lengths.size(), 0),</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  num_values,</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="stringliteral">"Sum of lengths should be equal to the total number of samples"</span>);</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  values_tensor.Resize(num_values);</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  percentiles_tensor.Resize(num_values);</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keywordtype">float</span>* values_tensor_data = values_tensor.template mutable_data<float>();</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keywordtype">float</span>* percentiles_tensor_data =</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  percentiles_tensor.template mutable_data<float>();</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> ind = 0; ind < num_values; ind++) {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  values_tensor_data[ind] = value_pct_data[2 * ind];</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  percentiles_tensor_data[ind] = value_pct_data[2 * ind + 1];</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  }</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keyword">auto</span>* percentile_values = Output(PCT);</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  percentile_values->ResizeLike(original_values);</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keywordtype">float</span>* percentile_values_data =</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  percentile_values->template mutable_data<float>();</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keywordtype">int</span> current_ind = 0;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordtype">int</span> current_dist_start = 0;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordtype">int</span> current_length;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < num_examples; i++) {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  current_dist_start = 0;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < num_features; j++) {</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  current_length = lengths_data[j];</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">const</span> <span class="keyword">auto</span> lower_bound =</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  std::lower_bound(</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  values_tensor_data + current_dist_start,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  values_tensor_data + current_dist_start + current_length,</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  original_values_data[current_ind]) -</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  values_tensor_data;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">if</span> (lower_bound == current_dist_start + current_length) {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  percentile_values_data[current_ind] = 1.0;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  original_values_data[current_ind] ==</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  values_tensor_data[lower_bound]) {</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  percentile_values_data[current_ind] =</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  percentiles_tensor_data[lower_bound];</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (lower_bound == current_dist_start) {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  percentile_values_data[current_ind] = 0.0;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keywordtype">float</span> lower_pct = percentiles_tensor_data[lower_bound - 1];</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordtype">float</span> upper_pct = percentiles_tensor_data[lower_bound];</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordtype">float</span> interval_length = values_tensor_data[lower_bound] -</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  values_tensor_data[lower_bound - 1];</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordtype">float</span> normalized_dist_to_lower = (original_values_data[current_ind] -</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  values_tensor_data[lower_bound - 1]) /</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  interval_length;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  percentile_values_data[current_ind] =</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  lower_pct + normalized_dist_to_lower * (upper_pct - lower_pct);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  }</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  current_dist_start += current_length;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  current_ind++;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  }</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  }</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> }</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> REGISTER_CPU_OPERATOR(Percentile, PercentileOp<CPUContext>);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> OPERATOR_SCHEMA(Percentile)</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  .NumInputs(3)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  .NumOutputs(1)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  .SetDoc(R<span class="stringliteral">"DOC(</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> <span class="stringliteral"> This operator is used to find percentile representations for raw values, given a sample</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> <span class="stringliteral"> set of raw values, labeled with their corresponding percentiles from the same distribution.</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> <span class="stringliteral"> In particular, this operator takes as input a tensor of floats to find the percentile values</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="stringliteral"> for, a 2D tensor of floats, where the first column of the tensor represents sampled values,</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="stringliteral"> and the second column represents the percentile labels, and a tensor of integers lengths.</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="stringliteral"></span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="stringliteral"> This lengths tensor is used because the operator works on multiple sets of raw values at the same time. For</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> <span class="stringliteral"> example, for an input:</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="stringliteral"> original_values=[[3, 5, 3],[5, 1, 6]], lengths = [2, 1, 1], value_to_pct = [[3, 0.2], [5, 0.5], [1, 0.3], [3. 0.6]]</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="stringliteral"></span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="stringliteral"> Our operator expects that each column i of the input tensor is sampled from distribution i. Lengths tells</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> <span class="stringliteral"> us that the first two elements in value_to_pct are sampled from distribution 1, the next is from distribution two,</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <span class="stringliteral"> and the last is from distribution 3. We expect the output of our operator to give us [[0.2, 1.0, 0.6], [0.5, 0.3, 1.0]].</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> <span class="stringliteral"></span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> <span class="stringliteral"> To calculate the percentile of an element, we check to see if its value is already mapped to</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> <span class="stringliteral"> a percentile in value_to_pct. If so, we return that value. If not, we linearly interpolate between</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> <span class="stringliteral"> the two closest values in value_to_pct. If the value is larger than all values in value_to_pct, we</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <span class="stringliteral"> return 1. If it's smaller than all the values, we return 0.</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <span class="stringliteral"></span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> <span class="stringliteral">)DOC")</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> <span class="stringliteral"> .Input(</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> <span class="stringliteral"> 0,</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="stringliteral"> </span><span class="stringliteral">"original_values"</span>,</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="stringliteral">"Input 2D tensor of floats, representing the original, raw data to calculate percentiles for."</span>)</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  .Input(</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  1,</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="stringliteral">"value_to_pct"</span>,</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="stringliteral">"Sorted 2D tensor, with 2 columns. Each element in the first column is a float representing the"</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="stringliteral">" raw value of a sample. Its corresponding element in the next column represents the percentile it maps to."</span>)</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  .Input(</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  2,</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="stringliteral">"lengths"</span>,</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="stringliteral">"1D tensor, representing the length of each distribution. We expect that the sum of elements of this tensor"</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="stringliteral">" is equal to the total length of value_to_pct."</span>)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  .Output(</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  0,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="stringliteral">"percentile_values"</span>,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="stringliteral">"1D tensor of floats, with the same dimensions as the flattened input tensor. Each element "</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="stringliteral">"of this tensor, percentile_values[i], corresponds to the percentile calculated "</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="stringliteral">"for original_values[i]."</span>);</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> NO_GRADIENT(Percentile);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> } <span class="comment">// namespace caffe2</span></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>
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