Merge pull request #3977 from PaddlePaddle/Adaptive_data_structure_for_SwitchOrderLayer

Adaptive data structure for switch order layer

paddle/gserver/layers/SwitchOrderLayer.cpp

@@ -24,10 +24,12 @@ bool SwitchOrderLayer::init(const LayerMap& layerMap,
   /* Initialize the basic parent class */
   Layer::init(layerMap, parameterMap);
   auto& img_conf = config_.inputs(0).image_conf();
+  size_t inD = img_conf.img_size_z();
   size_t inH =
       img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size();
   size_t inW = img_conf.img_size();
   size_t inC = img_conf.channels();
+  inH = inH * inD;
   inDims_ = TensorShape({0, inC, inH, inW});
   outDims_ = TensorShape(4);
 
@@ -64,9 +66,10 @@ void SwitchOrderLayer::setInDims() {
   MatrixPtr input = inputLayers_[0]->getOutputValue();
   size_t batchSize = input->getHeight();
   inDims_.setDim(0, batchSize);
-
+  int d = inputLayers_[0]->getOutput().getFrameDepth();
+  d = (d == 0 ? 1 : d);
   int h = inputLayers_[0]->getOutput().getFrameHeight();
-  if (h != 0) inDims_.setDim(2, h);
+  if (h != 0) inDims_.setDim(2, h * d);
   int w = inputLayers_[0]->getOutput().getFrameWidth();
   if (w != 0) inDims_.setDim(3, w);
   int totalCount = input->getElementCnt();

proto/ModelConfig.proto

@@ -271,6 +271,7 @@ message ImageConfig {
   // The size of input feature map.
   required uint32 img_size = 8;
   optional uint32 img_size_y = 9;
+  optional uint32 img_size_z = 10 [ default = 1 ];
 }
 
 message PriorBoxConfig {

python/paddle/trainer_config_helpers/layers.py

@@ -6414,7 +6414,7 @@ def gated_unit_layer(input,
 @wrap_name_default('switch_order')
 def switch_order_layer(input,
                        name=None,
-                       reshape=None,
+                       reshape_axis=None,
                        act=None,
                        layer_attr=None):
     """
@@ -6425,8 +6425,9 @@ def switch_order_layer(input,
     The example usage is:
 
     .. code-block:: python
+       reshape_axis = 3
+       switch = switch_order(input=layer, name='switch', reshape_axis=reshape_axis)
        reshape = {'height':[ 0, 1, 2], 'width':[3]}
-       switch = switch_order(input=layer, name='switch', reshape=reshape)
 
     :param input: The input layer.
     :type input: LayerOutput
@@ -6438,6 +6439,11 @@ def switch_order_layer(input,
     :rtype: LayerOutput
     """
     assert isinstance(input, LayerOutput)
+    assert reshape_axis != None and (reshape_axis > 0 and reshape_axis < 4)
+    height = [ele for ele in xrange(reshape_axis)]
+    width = [ele for ele in range(reshape_axis, 4)]
+    reshape = {'height': height, 'width': width}
+
     l = Layer(
         name=name,
         inputs=input.name,