Merge pull request #1136 from vpisarev:dnn5

Author: vpisarev

modules/dnn/samples/torch_enet.cpp

@@ -98,14 +98,19 @@ int main(int argc, char **argv)
     net.setBlob("", inputBlob);        //set the network input
     //! [Set input blob]
 
+    const int N = 3;
     TickMeter tm;
-    tm.start();
 
     //! [Make forward pass]
-    net.forward();                          //compute output
-    //! [Make forward pass]
-
-    tm.stop();
+    for( int i = 0; i < N; i++ )
+    {
+        TickMeter tm_;
+        tm_.start();
+        net.forward();                          //compute output
+        tm_.stop();
+        if( i == 0 || tm_.getTimeTicks() < tm.getTimeTicks() )
+            tm = tm_;
+    }
 
     //! [Gather output]
 

modules/dnn/src/layers/batch_norm_layer.cpp

@@ -41,6 +41,15 @@ class BatchNormLayerImpl : public BatchNormLayer
             Mat* inp = inputs[i];
             outputs[i].create(inp->dims, &inp->size.p[0], inp->type());
         }
+
+        varMeanScale = 1.f;
+        if (!hasWeights && !hasBias) {
+            varMeanScale = *blobs[2].ptr<float>();
+            if (varMeanScale != 0)
+                varMeanScale = 1/varMeanScale;
+        }
+
+        cv::pow(blobs[1]*varMeanScale + epsilon, -0.5, invStdMat);
     }
 
     void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
@@ -52,16 +61,6 @@ class BatchNormLayerImpl : public BatchNormLayer
         int weightsBlobIndex = 2;
         int biasBlobIndex = weightsBlobIndex + hasWeights;
 
-        float varMeanScale = 1;
-        if (!hasWeights && !hasBias) {
-            varMeanScale = *blobs[2].ptr<float>();
-            if (varMeanScale != 0)
-                varMeanScale = 1/varMeanScale;
-        }
-
-        Mat invStdMat;
-        cv::pow(blobs[1]*varMeanScale + epsilon, -0.5, invStdMat);
-
         int rows = inpBlob.size[2];
         int cols = inpBlob.size[3];
 
@@ -92,7 +91,8 @@ class BatchNormLayerImpl : public BatchNormLayer
     }
 
     bool hasWeights, hasBias;
-    float epsilon;
+    float epsilon, varMeanScale;
+    Mat invStdMat;
 };
 
 Ptr<BatchNormLayer> BatchNormLayer::create(const LayerParams& params)