diff --git a/paddle/gserver/layers/ConvBaseLayerCpu.cpp b/paddle/gserver/layers/ConvBaseLayerCpu.cpp
index 0da92bf0485b00898583eb1c4a4ce0387c376b36..13521bcd2b209eec78c3d7f7c5d03aeddb1da405 100644
--- a/paddle/gserver/layers/ConvBaseLayerCpu.cpp
+++ b/paddle/gserver/layers/ConvBaseLayerCpu.cpp
@@ -22,11 +22,20 @@ bool ConvBaseLayerCpu::init(const LayerMap &layerMap,
   /* Initialize the basic convolutional parent class */
   ConvBaseLayer::init(layerMap, parameterMap);
 
+  /* The class fields channels_ and numFilters_ are the same as in the config
+   * i.e., channels_ is the for the input and numFilters_ is for the output
+   *
+   * But in order for the variables in convTrans having the same semantic
+   * meaning as in conv, we need to swap channels_ and numFilters here for
+   * convTrans, and in other functions too.
+   * */
   int channel;
+  int nf;
   /* Initialize the projection */
   for (auto &inputConfig : config_.inputs()) {
     const ConvConfig &conf = inputConfig.conv_conf();
-    subM_.push_back(numFilters_ / conf.groups());
+    nf = isConv_ ? numFilters_ : conf.channels();
+    subM_.push_back(nf / conf.groups());
     subN_.push_back(conf.output_x() * conf.output_x());
     channel = isConv_ ? conf.channels() : numFilters_;
     subK_.push_back(channel * conf.filter_size() * conf.filter_size() /
@@ -123,20 +132,19 @@ void ConvBaseLayerCpu::expandFwdOnce(MatrixPtr image, MatrixPtr out,
   }
 }
 
-void ConvBaseLayerCpu::bpropActs(MatrixPtr image, MatrixPtr out, int inpIdx) {
+void ConvBaseLayerCpu::bpropActs(MatrixPtr out, MatrixPtr image, int inpIdx) {
   int channel = isConv_ ? channels_[inpIdx] : numFilters_;
 
   int subM = subM_[inpIdx];
   int subN = subN_[inpIdx];
   int subK = subK_[inpIdx];
   size_t batchSize = image->getHeight();
-  MatrixPtr tgtGrad = out;
 
   /* reset the expand-grad memory */
   resetExpandInput(subK * groups_[inpIdx], subN);
 
-  real *localGradData = image->getData();
-  real *tgtGradData = tgtGrad->getData();
+  real *localGradData = out->getData();
+  real *tgtGradData = image->getData();
   for (size_t n = 0; n < batchSize; n++) {
     real *wgtData = weights_[inpIdx]->getW()->getData();
     real *expandInData = expandInput_->getData();