HuberTwoClassification only support one dimension

paddle/gserver/layers/CostLayer.cpp

@@ -672,10 +672,10 @@ void HuberTwoClassification::forwardImp(Matrix& output,
                                         Matrix& target) {
   HuberCost::forwardImp(output, label, target);
   size_t numSamples = target.getHeight();
-  size_t dim = output.getWidth();
   CHECK(label.ids);
   CHECK_EQ((*label.ids).getSize(), numSamples);
   CHECK_EQ(output.getHeight(), numSamples);
+  CHECK_EQ(output.getWidth(), (size_t)1);
   CHECK_EQ(target.getWidth(), (size_t)1);
 
   real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData();
@@ -683,14 +683,11 @@ void HuberTwoClassification::forwardImp(Matrix& output,
   std::vector<real> cost(numSamples, 0);
   for (size_t i = 0; i < numSamples; ++i) {
     int y = 2 * lbl[i] - 1;
-    for (size_t j = 0; j < dim; ++j) {
-      int index = i * dim + j;
-      real a = out[index] * y;
-      if (a < -1)
-        cost[i] += -4 * a;
-      else if (a < 1)
-        cost[i] += (1 - a) * (1 - a);
-    }
+    real a = out[i] * y;
+    if (a < -1)
+      cost[i] = -4 * a;
+    else if (a < 1)
+      cost[i] = (1 - a) * (1 - a);
   }
   target.copyFrom(cost.data(), numSamples);
 }
@@ -699,22 +696,18 @@ void HuberTwoClassification::backwardImp(Matrix& output,
                                          Argument& label,
                                          Matrix& outputG) {
   size_t numSamples = output.getHeight();
-  size_t dim = output.getWidth();
   real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData();
   int* lbl = useGpu_ ? tmpCpuInput_[1].ids->getData() : (*label.ids).getData();
   real* grad = useGpu_ ? tmpCpuInput_[0].grad->getData() : outputG.getData();
   for (size_t i = 0; i < numSamples; ++i) {
     int y = 2 * lbl[i] - 1;
-    for (size_t j = 0; j < dim; ++j) {
-      int index = i * dim + j;
-      real a = out[index] * y;
-      if (a < -1)
-        grad[index] += -4 * y;
-      else if (a < 1)
-        grad[index] += -2 * (1 - a) * y;
-    }
+    real a = out[i] * y;
+    if (a < -1)
+      grad[i] += -4 * y;
+    else if (a < 1)
+      grad[i] += -2 * (1 - a) * y;
   }
-  if (useGpu_) outputG.copyFrom(grad, numSamples * dim);
+  if (useGpu_) outputG.copyFrom(grad, numSamples);
 }
 /**
  * This cost layer compute the sum of its input as loss.