Refine code and comments for CRMNormLayer.

ISSUE=4612671

git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1501 1ad973e4-5ce8-4261-8a94-b56d1f490c56

doc/ui/api/trainer_config_helpers/layers.rst

@@ -82,12 +82,6 @@ img_cmrnorm_layer
     :members: img_cmrnorm_layer
     :noindex:
 
-img_rnorm_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: img_rnorm_layer
-    :noindex:
-
 batch_norm_layer
 ---------------------
 ..  automodule:: paddle.trainer_config_helpers.layers

doc/ui/index.md

@@ -7,7 +7,7 @@
 
 ## API Reference
 
-* [Trainer Config Helpers](api/trainer_config_helpers/index.md)
+* [Model Config Interface](api/trainer_config_helpers/index.md)
 
 ## Command Line Argument
 

paddle/gserver/layers/NormProjectionLayer.cpp

@@ -46,9 +46,6 @@ bool CMRProjectionNormLayer::init(const LayerMap& layerMap,
   /* the size of inputs for norm-layer is 1 */
   CHECK_EQ(config_.inputs_size(), 1);
 
-  auto& inputConfig = config_.inputs(0);
-  blocked_ = inputConfig.norm_conf().blocked();
-
   return true;
 }
 
@@ -69,7 +66,7 @@ void CMRProjectionNormLayer::forward(PassType passType) {
   denoms_->zeroMem();
 
   outV->crossMapNormalFwd(*input, imgSizeH_, imgSizeW_, *denoms_, channels_,
-                          size_, scale_, pow_, blocked_);
+                          size_, scale_, pow_);
 }
 
 void CMRProjectionNormLayer::backward(const UpdateCallback& callback) {
@@ -86,6 +83,6 @@ void CMRProjectionNormLayer::backward(const UpdateCallback& callback) {
 
   preOutGrad->crossMapNormalBwd(*localGrad, *denoms_, *preOutV, *localOutV,
                                 channels_, imgSizeH_, imgSizeW_, size_, scale_,
-                                pow_, blocked_);
+                                pow_);
 }
 }  // namespace paddle

paddle/gserver/layers/NormProjectionLayer.h

@@ -23,15 +23,12 @@ namespace paddle {
 
 /**
  * @brief response normalization across feature maps
- * namely normalize in number of size_ channels 
+ * namely normalize in number of size_ channels
  */
 class CMRProjectionNormLayer : public ResponseNormLayer {
   size_t imgSizeH_, imgSizeW_;
   size_t outputH_, outputW_;
 
-protected:
-  bool blocked_;
-
 public:
   explicit CMRProjectionNormLayer(const LayerConfig& config)
       : ResponseNormLayer(config) {}

paddle/math/Matrix.cpp

@@ -943,7 +943,7 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad, size_t imgSizeH,
 void GpuMatrix::crossMapNormalFwd(Matrix& input, size_t imgSizeH,
                                   size_t imgSizeW, Matrix& denoms,
                                   size_t channels, size_t sizeX, float scale,
-                                  float pow, bool blocked) {
+                                  float pow) {
   size_t num = input.getHeight();
   size_t height = imgSizeH;
   size_t width = imgSizeW;
@@ -960,7 +960,7 @@ void GpuMatrix::crossMapNormalBwd(Matrix& localGrad, Matrix& denoms,
                                   Matrix& preOutV, Matrix& localOutV,
                                   size_t channels, size_t imgSizeH,
                                   size_t imgSizeW, size_t sizeX, float scale,
-                                  float pow, bool blocked) {
+                                  float pow) {
   size_t num = preOutV.getHeight();
   size_t height = imgSizeH;
   size_t width = imgSizeW;
@@ -1602,7 +1602,7 @@ void CpuMatrix::avgPoolBackward(Matrix& input, size_t imgSizeH, size_t imgSizeW,
 void CpuMatrix::crossMapNormalFwd(Matrix& input, size_t imgSizeH,
                                   size_t imgSizeW, Matrix& denoms,
                                   size_t channels, size_t sizeX, float scale,
-                                  float pow, bool blocked) {
+                                  float pow) {
   size_t num = input.getHeight();
   size_t height = imgSizeH;
   size_t width = imgSizeW;
@@ -1655,7 +1655,7 @@ void CpuMatrix::crossMapNormalBwd(Matrix& localGrad, Matrix& denoms,
                                   Matrix& preOutV, Matrix& localOutV,
                                   size_t channels, size_t imgSizeH,
                                   size_t imgSizeW, size_t size, float scale,
-                                  float pow, bool blocked) {
+                                  float pow) {
   LOG(FATAL) << "Not implemented";
 
   CHECK(imgSizeH * imgSizeW * channels == preOutV.getWidth());

paddle/math/Matrix.h

@@ -585,7 +585,7 @@ public:
    * \f[
    *  a[i] = \sum_{j=-(N-1)/2}^{(N-1)/2} b_{i+j} * c_{j}
    * \f]
-   *  
+   *
    * b contains M elements,
    * c contains N elements (N is odd),
    * b's index arithmetic is computed modulo M,
@@ -774,7 +774,7 @@ public:
   virtual void crossMapNormalFwd(Matrix& input, size_t imgSizeH,
                                  size_t imgSizeW, Matrix& denoms,
                                  size_t channels, size_t sizeX, float scale,
-                                 float pow, bool blocked) {
+                                 float pow) {
     LOG(FATAL) << "Not implemeted";
   }
 
@@ -782,7 +782,7 @@ public:
                                  Matrix& preOutV, Matrix& localOutV,
                                  size_t channels, size_t imgSizeH,
                                  size_t imgSizeW, size_t size, float scale,
-                                 float pow, bool blocked) {
+                                 float pow) {
     LOG(FATAL) << "Not implemeted";
   }
 
@@ -883,7 +883,7 @@ public:
    * @code
    * this[i] = -sum(label[i][j]*log(output[i][j])
    *           + (1-label[i][j])*log(1-output[i][j]))
-   * @endcode             
+   * @endcode
    */
   virtual void multiBinaryLabelCrossEntropy(Matrix& output, Matrix& label) {
     LOG(FATAL) << "Not implemented";
@@ -895,7 +895,7 @@ public:
    * @code
    * this[i][j] = -label[i][j]/output[i][j]
    *              + (1-label[i][j])/(1-output[i][j])
-   * @endcode             
+   * @endcode
    */
   virtual void multiBinaryLabelCrossEntropyBp(Matrix& output, Matrix& label) {
     LOG(FATAL) << "Not implemented";
@@ -903,12 +903,12 @@ public:
 
   /**
    * @brief  Calculate the classification error for multi binary labels
-   * 
+   *
    * @code
    * this[i] = sum((output[i][j] >= threshold && label[i][j] == 0)
    *            || (output[i][j] < threshold && label[i][j] == 1))
    *            / output->getWidth()
-   * @endcode           
+   * @endcode
    */
   virtual void classificationErrorMulti(Matrix& output, Matrix& label,
                                         real threshold) {
@@ -1149,12 +1149,12 @@ public:
 
   void crossMapNormalFwd(Matrix& input, size_t imgSizeH, size_t imgSizeW,
                          Matrix& denoms, size_t channels, size_t sizeX,
-                         float scale, float pow, bool blocked);
+                         float scale, float pow);
 
   void crossMapNormalBwd(Matrix& localGrad, Matrix& denoms, Matrix& preOutV,
                          Matrix& localOutV, size_t channels, size_t imgSizeH,
-                         size_t imgSizeW, size_t sizeX, float scale, float pow,
-                         bool blocked);
+                         size_t imgSizeW, size_t sizeX,
+                         float scale, float pow);
 
   void maxSequenceForward(Matrix& input, const IVector& sequence,
                           IVector& index);
@@ -1260,12 +1260,12 @@ public:
 
   void crossMapNormalFwd(Matrix& input, size_t imgSizeH, size_t imgSizeW,
                          Matrix& denoms, size_t channels, size_t sizeX,
-                         float scale, float pow, bool blocked);
+                         float scale, float pow);
 
   void crossMapNormalBwd(Matrix& localGrad, Matrix& denoms, Matrix& preOutV,
                          Matrix& localOutV, size_t channels, size_t imgSizeH,
-                         size_t imgSizeW, size_t sizeX, float scale, float pow,
-                         bool blocked);
+                         size_t imgSizeW, size_t sizeX,
+                         float scale, float pow);
 
   void maxSequenceForward(Matrix& input, const IVector& sequence,
                           IVector& index);
@@ -1307,14 +1307,14 @@ public:
    * @code
    * table.row[ids[i]] += this.row[i]
    * @endcode
-   */ 
+   */
   virtual void addToRows(Matrix& table, IVector& ids);
 
   /**
    * @code
    * this[i] = table[i, id[i]]
    * @endcode
-   */ 
+   */
   virtual void selectElements(Matrix& table, IVector& ids);
 
   /**

python/paddle/trainer_config_helpers/layers.py

@@ -1500,7 +1500,7 @@ def img_pool_layer(input, pool_size, name=None,
 
 
 def __img_norm_layer__(name, input, size, norm_type, scale, power,
-                       num_channels, blocked, layer_attr):
+                       num_channels, blocked=0, layer_attr):
     if num_channels is None:
         assert input.num_filters is not None
         num_channels = input.num_filters
@@ -1522,9 +1522,9 @@ def __img_norm_layer__(name, input, size, norm_type, scale, power,
 @layer_support()
 def img_cmrnorm_layer(input, size, scale=0.0128, power=0.75,
                       name=None, num_channels=None,
-                      blocked=0, layer_attr=None):
+                      layer_attr=None):
     """
-    Convolution cross-map-response-normalize layer.
+    Response normalization across feature maps.
     The details please refer to
     `Alex's paper <http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf>`_.
 
@@ -1532,7 +1532,7 @@ def img_cmrnorm_layer(input, size, scale=0.0128, power=0.75,
     :type name: None|basestring
     :param input: layer's input.
     :type input: LayerOutput
-    :param size: cross map response size.
+    :param size: Normalize in number of :math:`size` feature maps.
     :type size: int
     :param scale: The hyper-parameter.
     :type scale: float
@@ -1547,30 +1547,7 @@ def img_cmrnorm_layer(input, size, scale=0.0128, power=0.75,
     :rtype: LayerOutput
     """
     return __img_norm_layer__(name, input, size, "cmrnorm-projection", scale,
-                              power, num_channels, blocked, layer_attr)
-
-
-@wrap_name_default("rnorm")
-@layer_support()
-def img_rnorm_layer(input, size, scale, power, name=None, num_channels=None,
-                    layer_attr=None):
-    """
-    Normalize the input in local region, namely response normalization
-    across feature maps.
-
-    :param name: The name of this layer.
-    :rtype name: None|basestring
-    :param input: The input of this layer.
-    :param size:
-    :param scale:
-    :param power:
-    :param num_channels:
-    :param layer_attr:
-    :return: LayerOutput object.
-    :rtype: LayerOutput
-    """
-    return __img_norm_layer__(name, input, size, 'rnorm', scale, power,
-                              num_channels, 0, layer_attr)
+                              power, num_channels, 0, layer_attr)
 
 
 @wrap_bias_attr_default()