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<div class="title">spatial_batch_norm_gradient_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/spatial_batch_norm_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> SpatialBNGradientOp<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>& X = Input(INPUT);</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& dY = Input(OUTPUT_GRAD);</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& scale = Input(SCALE);</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>  CAFFE_ENFORCE(X.ndim() >= 3 && X.ndim() <= 5);</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> N = X.dim32(0);</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> C =</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  (order_ == StorageOrder::NCHW ? X.dim32(1) : X.dim32(X.ndim() - 1));</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> H = (order_ == StorageOrder::NCHW ? X.dim32(2) : X.dim32(1));</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> W = X.ndim() > 3</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  ? (order_ == StorageOrder::NCHW ? X.dim32(3) : X.dim32(2))</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  : 1;</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> D = X.ndim() > 4</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  ? (order_ == StorageOrder::NCHW ? X.dim32(4) : X.dim32(3))</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  : 1;</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>  <span class="keyword">const</span> <span class="keywordtype">int</span> sample_size = H * W * D;</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  CAFFE_ENFORCE_EQ(scale.ndim(), 1);</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  CAFFE_ENFORCE_EQ(scale.dim32(0), C);</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>  ConstEigenVectorArrayMap<float> scale_arr(scale.data<<span class="keywordtype">float</span>>(), C);</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  ConstEigenVectorArrayMap<float> mean_arr(Input(SAVED_MEAN).data<float>(), C);</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  ConstEigenVectorArrayMap<float> inv_var_arr(</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  Input(SAVED_INV_VAR).data<float>(), C);</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keyword">auto</span>* dX = Output(INPUT_GRAD);</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  dX->ResizeLike(X);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keyword">auto</span>* dScale = Output(SCALE_GRAD);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keyword">auto</span>* dBias = Output(BIAS_GRAD);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keywordflow">if</span> (num_batches_ == 1) {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  dScale->ResizeLike(scale);</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  dBias->ResizeLike(scale);</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> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="comment">// dBias = np.sum(dY, axis=0)</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="comment">// dScale = np.sum((X - mean) / inv_std * dy, axis=0)</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="comment">// dX = (1. / N) * scale * inv_var * (N * dY - np.sum(dY, axis=0) - (X - mean)</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="comment">// * inv_var * inv_var * np.sum(dY * (X - mean), axis=0))</span></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>  EigenVectorArrayMap<float> dBias_arr(dBias->mutable_data<<span class="keywordtype">float</span>>(), C);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  EigenVectorArrayMap<float> dScale_arr(dScale->mutable_data<<span class="keywordtype">float</span>>(), C);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">if</span> (num_batches_ == 1) {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  dBias_arr.setZero();</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  dScale_arr.setZero();</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  }</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keyword">const</span> <span class="keyword">auto</span> scaleInvVarNHW = scale_arr * inv_var_arr / (N * sample_size);</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="keywordflow">switch</span> (order_) {</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">case</span> StorageOrder::NCHW: {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  ConstEigenArrayMap<float> X_arr(X.data<<span class="keywordtype">float</span>>(), sample_size, N * C);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  ConstEigenArrayMap<float> dY_arr(dY.data<<span class="keywordtype">float</span>>(), sample_size, N * C);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  EigenArrayMap<float> dX_arr(</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  dX->mutable_data<<span class="keywordtype">float</span>>(), sample_size, N * C);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  dX_arr.setZero();</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="keywordflow">if</span> (num_batches_ == 1) {</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> nc = 0; nc < N * C; ++nc) {</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordtype">int</span> c = nc % C;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  dBias_arr(c) += dY_arr.col(nc).sum();</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  dScale_arr(c) +=</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  ((X_arr.col(nc) - mean_arr(c)) * inv_var_arr(c) * dY_arr.col(nc))</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  .sum();</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  }</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> c = 0; c < C; ++c) {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  dBias_arr(c) /= num_batches_;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  dScale_arr(c) /= num_batches_;</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">for</span> (<span class="keywordtype">int</span> nc = 0; nc < N * C; ++nc) {</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordtype">int</span> c = nc % C;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  dX_arr.col(nc) += scaleInvVarNHW(c) *</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  (dY_arr.col(nc) * N * sample_size - dBias_arr(c) -</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  (X_arr.col(nc) - mean_arr[c]) * dScale_arr(c) * inv_var_arr(c));</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  }</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  }</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">case</span> StorageOrder::NHWC: {</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  ConstEigenArrayMap<float> X_arr(X.data<<span class="keywordtype">float</span>>(), C, N * sample_size);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  ConstEigenArrayMap<float> dY_arr(dY.data<<span class="keywordtype">float</span>>(), C, N * sample_size);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  EigenArrayMap<float> dX_arr(</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  dX->mutable_data<<span class="keywordtype">float</span>>(), C, N * sample_size);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  dX_arr.setZero();</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keyword">const</span> <span class="keyword">auto</span> dYRowSum = dY_arr.rowwise().sum();</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keyword">const</span> <span class="keyword">auto</span> XMinusMean = X_arr.colwise() - mean_arr;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keyword">const</span> <span class="keyword">auto</span> dYMulXMinusMeanRowSum = (dY_arr * XMinusMean).rowwise().sum();</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keyword">const</span> <span class="keyword">auto</span> invVarSqr = inv_var_arr * inv_var_arr;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> nhw = 0; nhw < N * sample_size; ++nhw) {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  dBias_arr += dY_arr.col(nhw);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  dScale_arr +=</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  (X_arr.col(nhw) - mean_arr) * inv_var_arr * dY_arr.col(nhw);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  dX_arr.col(nhw) += scaleInvVarNHW *</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  (dY_arr.col(nhw) * N * sample_size - dYRowSum -</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  XMinusMean.col(nhw) * invVarSqr * dYMulXMinusMeanRowSum);</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>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  CAFFE_THROW(<span class="stringliteral">"Unknown storage order: "</span>, order_);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  }</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> }</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> REGISTER_CPU_OPERATOR(SpatialBNGradient, SpatialBNGradientOp<CPUContext>);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> <span class="comment">// Input: X, scale, dY, mean, variance, dscale, dbias</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="comment">// Output: dX, dscale, dbias</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> OPERATOR_SCHEMA(SpatialBNGradient)</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  .NumInputs({5, 7})</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  .NumOutputs(3)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  .AllowInplace({{5, 1}, {6, 2}});</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> <span class="comment">// Spatial batch normalization's gradient, depending on the various input sizes,</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> <span class="comment">// is a bit more complex than usual gradient operators.</span></div><div class="line"><a name="l00127"></a><span class="lineno"><a class="line" href="classcaffe2_1_1_get_spatial_b_n_gradient.html"> 127</a></span> <span class="keyword">class </span><a class="code" href="classcaffe2_1_1_get_spatial_b_n_gradient.html">GetSpatialBNGradient</a> : <span class="keyword">public</span> <a class="code" href="classcaffe2_1_1_gradient_maker_base.html">GradientMakerBase</a> {</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keyword">using</span> GradientMakerBase::GradientMakerBase;</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  vector<OperatorDef> GetGradientDefs()<span class="keyword"> override </span>{</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="comment">// Check if we are in training or testing mode.</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordtype">bool</span> is_test =</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  ArgumentHelper::GetSingleArgument(def_, OpSchema::Arg_IsTest, 0);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordtype">int</span> num_batches = ArgumentHelper::GetSingleArgument(def_, <span class="stringliteral">"num_batches"</span>, 1);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  vector<string> grad_outputs{GI(0), GI(1), GI(2)};</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  vector<string> grad_inputs;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keywordflow">if</span> (is_test) {</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// This is in testing mode. The operator should have five inputs:</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="comment">// X, scale, bias, estimated_mean, estimated_variance</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="comment">// The gradient inputs are:</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="comment">// X, scale, dY, estimated_mean, estimated_variance</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  CAFFE_ENFORCE_EQ(def_.input_size(), 5);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  CAFFE_ENFORCE_EQ(def_.output_size(), 1);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  grad_inputs = vector<string>{I(0), I(1), GO(0), I(3), I(4)};</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (num_batches > 1) {</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  CAFFE_ENFORCE_EQ(def_.input_size(), 7);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  CAFFE_ENFORCE_EQ(def_.output_size(), 5);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  grad_inputs = vector<string>{I(0), I(1), GO(0), O(3), O(4), GI(1), GI(2)};</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  CAFFE_ENFORCE_EQ(def_.input_size(), 5);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  CAFFE_ENFORCE_EQ(def_.output_size(), 5);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  grad_inputs = vector<string>{I(0), I(1), GO(0), O(3), O(4)};</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  }</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordflow">return</span> <a class="code" href="classcaffe2_1_1_gradient_maker_base.html#a44d7fb1a86d355a0d057648443f2d1f7">SingleGradientDef</a>(</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="stringliteral">"SpatialBNGradient"</span>, <span class="stringliteral">""</span>, grad_inputs, grad_outputs);</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> };</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> REGISTER_GRADIENT(SpatialBN, <a class="code" href="classcaffe2_1_1_get_spatial_b_n_gradient.html">GetSpatialBNGradient</a>);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> }</div><div class="ttc" id="classcaffe2_1_1_gradient_maker_base_html"><div class="ttname"><a href="classcaffe2_1_1_gradient_maker_base.html">caffe2::GradientMakerBase</a></div><div class="ttdef"><b>Definition:</b> <a href="operator__gradient_8h_source.html#l00047">operator_gradient.h:47</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_gradient_maker_base_html_a44d7fb1a86d355a0d057648443f2d1f7"><div class="ttname"><a href="classcaffe2_1_1_gradient_maker_base.html#a44d7fb1a86d355a0d057648443f2d1f7">caffe2::GradientMakerBase::SingleGradientDef</a></div><div class="ttdeci">static vector< OperatorDef > SingleGradientDef(const Args &...args)</div><div class="ttdoc">a helper function to allow one to create one single operator def, which is usually the case for many ...</div><div class="ttdef"><b>Definition:</b> <a href="operator__gradient_8h_source.html#l00199">operator_gradient.h:199</a></div></div>
<div class="ttc" id="classcaffe2_1_1_get_spatial_b_n_gradient_html"><div class="ttname"><a href="classcaffe2_1_1_get_spatial_b_n_gradient.html">caffe2::GetSpatialBNGradient</a></div><div class="ttdef"><b>Definition:</b> <a href="spatial__batch__norm__gradient__op_8cc_source.html#l00127">spatial_batch_norm_gradient_op.cc:127</a></div></div>
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