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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="preprocessor">#include "softmax_with_loss_op.h"</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="preprocessor">#include "softmax_shared.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="keyword">namespace </span><a class="code" href="namespacecaffe2.html">caffe2</a> {</div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> REGISTER_CPU_OPERATOR(SoftmaxWithLoss, SoftmaxWithLossOp<float, CPUContext>);</div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> REGISTER_CPU_OPERATOR(</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>  SoftmaxWithLossGradient,</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>  SoftmaxWithLossGradientOp<float, CPUContext>);</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment">// Input: X (logits), T (labels); Output: P (probs), Y</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> OPERATOR_SCHEMA(SoftmaxWithLoss)</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>  .NumInputs(2, 3)</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  .NumOutputs(2)</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  .TensorInferenceFunction(</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  [](<span class="keyword">const</span> OperatorDef& def, <span class="keyword">const</span> vector<TensorShape>& in) {</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  ArgumentHelper helper(def);</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <span class="keyword">auto</span> axis = helper.GetSingleArgument<int32_t>(<span class="stringliteral">"axis"</span>, 1);</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>  vector<TensorShape> out(2);</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>  <span class="keyword">auto</span> logits = in[0]; <span class="comment">// Tensor with Shape [batch_size, num_classes]</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keyword">auto</span> labels = in[1]; <span class="comment">// Tensor with shape [batch_size, ]</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keyword">const</span> <span class="keyword">auto</span> canonical_axis =</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  canonical_axis_index_(axis, logits.dims().size());</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch_size =</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  size_to_dim_(canonical_axis, GetDimsVector(logits));</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_classes =</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <a class="code" href="namespacecaffe2.html#add14fa17af46b7f9a8a81cd9651456d6">size_from_dim_</a>(canonical_axis, GetDimsVector(logits));</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  out[0].set_data_type(logits.data_type());</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  out[0].add_dims(batch_size);</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  out[0].add_dims(num_classes);</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordflow">return</span> out;</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>  .SetDoc(R<span class="stringliteral">"DOC(</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="stringliteral">Combined Softmax and Cross-Entropy loss operator.</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="stringliteral">The operator computes the softmax normalized values for each layer in the batch</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="stringliteral">of the given input, after which cross-entropy loss is computed. This operator is</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="stringliteral">numerically more stable than separate Softmax and CrossEntropy ops.</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="stringliteral">The inputs are a 2-D tensor (Tensor<float>) of size</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="stringliteral">(batch_size x input_feature_dimensions) and tensor of labels (ground truth).</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="stringliteral">Output is tensor with the probability for each label for each example (N x D)</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="stringliteral">and averaged loss (scalar).</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="stringliteral">Use parameter label_prob=1 to enable inputting labels as a probability</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="stringliteral">distribution.</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="stringliteral">Optional third input blob can be used to weight the samples for the loss.</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="stringliteral">)DOC")</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="stringliteral"> .Input(0, </span><span class="stringliteral">"logits"</span>, <span class="stringliteral">"Unscaled log probabilities"</span>)</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  .Input(1, <span class="stringliteral">"labels"</span>, <span class="stringliteral">"Ground truth"</span>)</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  .Input(</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  2,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="stringliteral">"weight_tensor"</span>,</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="stringliteral">"Optional blob to be used to weight the samples for the loss."</span>)</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  .Output(0, <span class="stringliteral">"softmax"</span>, <span class="stringliteral">"Tensor with softmax cross entropy loss"</span>)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  .Output(1, <span class="stringliteral">"loss"</span>, <span class="stringliteral">"Average loss"</span>);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="comment">// Input: X, T, P, dY; Output: dX</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> OPERATOR_SCHEMA(SoftmaxWithLossGradient).NumOutputs(1);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="preprocessor">#define DONT_CARE (-1)</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="keyword">template</span> <></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> <span class="keywordtype">bool</span> SoftmaxWithLossOp<float, CPUContext>::RunOnDevice() {</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keyword">auto</span>& X = Input(0); <span class="comment">// Logits</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keyword">auto</span>& T = Input(1); <span class="comment">// Labels / targets</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keyword">auto</span>* P = Output(0); <span class="comment">// Probabilities from softmax</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">auto</span>* avg_loss = Output(1); <span class="comment">// Average loss</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keyword">const</span> <span class="keyword">auto</span> canonical_axis = X.canonical_axis_index(axis_);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordtype">int</span> N, D;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  N = X.size_to_dim(canonical_axis); <span class="comment">// batch size</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  D = X.size_from_dim(canonical_axis);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  P->ResizeLike(X);</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>  <span class="keywordflow">if</span> (sum_multiplier_.size() != D) {</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  sum_multiplier_.Resize(D);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  math::Set<float, CPUContext>(</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  D, 1.f, sum_multiplier_.mutable_data<<span class="keywordtype">float</span>>(), &context_);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  }</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordtype">float</span>* Pdata = P->mutable_data<<span class="keywordtype">float</span>>();</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>* weights = (InputSize() > 2 ? Input(2).data<<span class="keywordtype">float</span>>() : <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keywordflow">if</span> (label_prob_mode_) {</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  CAFFE_ENFORCE_GE(T.ndim(), 2);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  CAFFE_ENFORCE_EQ(T.size_to_dim(canonical_axis), N);</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  CAFFE_ENFORCE_EQ(T.size_from_dim(canonical_axis), D);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordflow">if</span> (T.ndim() == canonical_axis) {</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  CAFFE_ENFORCE_EQ(T.size(), N);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  CAFFE_ENFORCE_EQ(T.size_to_dim(canonical_axis), N);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  CAFFE_ENFORCE_EQ(T.size_from_dim(canonical_axis), 1);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  }</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  }</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordflow">if</span> (sum_multiplier_.size() != D) {</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  sum_multiplier_.Resize(D);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  math::Set<float, CPUContext>(</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  D, 1.f, sum_multiplier_.mutable_data<<span class="keywordtype">float</span>>(), &context_);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  }</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  rowmax_.Resize(N);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  losses_.Resize(N);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  SoftmaxCPU(</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  context_,</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  N,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  D,</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  X.data<<span class="keywordtype">float</span>>(),</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  Pdata,</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  losses_.mutable_data<<span class="keywordtype">float</span>>(),</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  sum_multiplier_.data<<span class="keywordtype">float</span>>(),</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  !label_prob_mode_,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  rowmax_.mutable_data<<span class="keywordtype">float</span>>());</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="comment">// Then compute cross entropy</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordtype">float</span> loss_sum = 0.0;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordtype">float</span> weight_sum = 0.0;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordflow">if</span> (!label_prob_mode_) {</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* label_data = T.data<<span class="keywordtype">int</span>>();</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>* Xdata = X.data<<span class="keywordtype">float</span>>();</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < N; ++i) {</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  CAFFE_ENFORCE(</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  label_data[i] < D && label_data[i] >= 0,</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="stringliteral">"Label seems incorrect: label value larger than number of classes: "</span>,</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  label_data[i],</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="stringliteral">" vs "</span>,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  D);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordtype">float</span> weight = weights ? weights[i] : 1.0;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordtype">float</span> l = -Pdata[i * D + label_data[i]] * weight;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  loss_sum += l;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  weight_sum += weight;</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  }</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  math::Exp(N * D, Pdata, Pdata, &context_);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>* label_data = T.data<<span class="keywordtype">float</span>>();</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < N; ++i) {</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordtype">float</span> l = 0.0;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keywordtype">float</span> total_prob = 0.0;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordtype">float</span> weight = weights ? weights[i] : 1.0;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < D; ++j) {</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  CAFFE_ENFORCE(</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  label_data[i * D + j] >= 0,</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="stringliteral">"Label prob seems incorrect: label prob value must be nonnegative:"</span>,</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="stringliteral">" "</span>,</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  label_data[i * D + j]);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  l += -log(std::max(Pdata[i * D + j], 1e-20f)) * label_data[i * D + j] *</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  weight;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  total_prob += label_data[i * D + j];</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  }</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  loss_sum += l;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  CAFFE_ENFORCE(</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  std::abs(total_prob - 1.) < 1e-5f,</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="stringliteral">"Label prob seems incorrect: label prob values do not sum to 1.0: "</span>,</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  total_prob,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="stringliteral">" vs 1.0 (+/- 1e-5)"</span>);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  weight_sum += weight;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  }</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>  avg_loss->Resize(vector<TIndex>());</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keywordtype">float</span>* avg_loss_data = avg_loss->mutable_data<<span class="keywordtype">float</span>>();</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">if</span> (weight_sum != 0.0) {</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  avg_loss_data[0] = loss_sum * scale_ / weight_sum;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  avg_loss_data[0] = 0.0;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  }</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> }</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> </div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> <span class="keyword">template</span> <></div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> <span class="keywordtype">bool</span> SoftmaxWithLossGradientOp<float, CPUContext>::RunOnDevice() {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keyword">auto</span>& X = Input(0); <span class="comment">// Logits</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keyword">auto</span>& T = Input(1); <span class="comment">// Labels / targets</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="comment">// Input(2) is weights if given</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keyword">auto</span>& P = Input(InputSize() - 2); <span class="comment">// Probabilities from softmax</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keyword">auto</span>& d_avg_loss = Input(InputSize() - 1); <span class="comment">// Gradient w.r.t. avg loss</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keyword">auto</span>* dX = Output(0);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>* weights = (InputSize() > 4 ? Input(2).data<<span class="keywordtype">float</span>>() : <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keyword">const</span> <span class="keyword">auto</span> canonical_axis = X.canonical_axis_index(axis_);</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keywordtype">int</span> N, D;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  N = X.size_to_dim(canonical_axis); <span class="comment">// batch size</span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  D = X.size_from_dim(canonical_axis);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  dX->ResizeLike(X);</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>  <span class="keywordflow">if</span> (label_prob_mode_) {</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  CAFFE_ENFORCE_GE(T.ndim(), 2);</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  CAFFE_ENFORCE_EQ(T.size_to_dim(canonical_axis), N);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  CAFFE_ENFORCE_EQ(T.size_from_dim(canonical_axis), D);</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordflow">if</span> (T.ndim() == canonical_axis) {</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  CAFFE_ENFORCE_EQ(T.size(), N);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  CAFFE_ENFORCE_EQ(T.size_to_dim(canonical_axis), N);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  CAFFE_ENFORCE_EQ(T.size_from_dim(canonical_axis), 1);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  }</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  }</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> </div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>* Pdata = P.data<<span class="keywordtype">float</span>>();</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordtype">float</span>* dX_data = dX->mutable_data<<span class="keywordtype">float</span>>();</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> </div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="comment">// Copy softmax probabilities into dX. All but the neuron</span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="comment">// corresponding to the correct label has gradient equaling e(x_j)</span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="comment">// which is the probability under softmax.</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  context_.Copy<float, CPUContext, CPUContext>(P.size(), Pdata, dX_data);</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="comment">// Compute gradient for the matching labels.</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordtype">float</span> total_weight = 0.0f;</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordflow">if</span> (!label_prob_mode_) {</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* label_data = T.data<<span class="keywordtype">int</span>>();</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keywordflow">if</span> (weights) {</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < N; ++i) {</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordtype">int</span> idx = i * D + label_data[i];</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordtype">float</span> weight = weights[i];</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  dX_data[idx] = Pdata[idx] - 1.0;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> d = 0; d < D; d++) {</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keywordtype">int</span> k = i * D + d;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  dX_data[k] *= weight;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  }</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  total_weight += weight;</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">else</span> {</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 < N; ++i) {</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordtype">int</span> idx = i * D + label_data[i];</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  dX_data[idx] = Pdata[idx] - 1.0f;</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>  total_weight = N;</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>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>* label_data = T.data<<span class="keywordtype">float</span>>();</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> </div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">if</span> (weights) {</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < N; ++i) {</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordtype">float</span> weight = weights[i];</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < D; ++j) {</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordtype">int</span> idx = i * D + j;</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  dX_data[idx] = (Pdata[idx] - label_data[idx]) * weight;</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  }</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  total_weight += weight;</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  }</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < N; ++i) {</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < D; ++j) {</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keywordtype">int</span> idx = i * D + j;</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  dX_data[idx] = Pdata[idx] - label_data[idx];</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  }</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  }</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  total_weight = N;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  }</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> </div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="comment">// Scale by d_avg_loss / N</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keywordflow">if</span> (total_weight > 0) {</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  math::Scale<float, CPUContext>(</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  dX->size(),</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  scale_ / total_weight * d_avg_loss.data<<span class="keywordtype">float</span>>()[0],</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  dX->data<<span class="keywordtype">float</span>>(),</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  dX_data,</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  &context_);</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  }</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> }</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> </div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> <span class="keyword">namespace </span>{</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> <span class="keyword">class </span>GetSoftmaxWithLossGradient : <span class="keyword">public</span> GradientMakerBase {</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keyword">using</span> GradientMakerBase::GradientMakerBase;</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  vector<OperatorDef> GetGradientDefs()<span class="keyword"> override </span>{</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  vector<string> blob_names{</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  {I(0), I(1), O(0), GO(1)},</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  };</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> </div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="comment">// Add weight blob, if given</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="keywordflow">if</span> (def_.input_size() == 3) {</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  blob_names.emplace(blob_names.begin() + 2, I(2));</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  }</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keywordflow">return</span> SingleGradientDef(</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="stringliteral">"SoftmaxWithLossGradient"</span>, <span class="stringliteral">""</span>, blob_names, vector<string>{GI(0)});</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  }</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</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> REGISTER_GRADIENT(SoftmaxWithLoss, GetSoftmaxWithLossGradient);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> }</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> } <span class="comment">// namespace caffe2</span></div><div class="ttc" id="namespacecaffe2_html_add14fa17af46b7f9a8a81cd9651456d6"><div class="ttname"><a href="namespacecaffe2.html#add14fa17af46b7f9a8a81cd9651456d6">caffe2::size_from_dim_</a></div><div class="ttdeci">TIndex size_from_dim_(int k, const vector< TIndex > &dims)</div><div class="ttdoc">Return product of all dimensions starting from K. </div><div class="ttdef"><b>Definition:</b> <a href="tensor_8h_source.html#l00040">tensor.h:40</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>
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