Reset OpenCL kernels if batch size changes

Author: dkurt

modules/dnn/src/layers/convolution_layer.cpp

@@ -273,6 +273,9 @@ class ConvolutionLayerImpl : public BaseConvolutionLayerImpl
             for(int i = 0; i < outCn; i++ )
                 biasvec[i] = biasMat.at<float>(i);
         }
+#ifdef HAVE_OPENCL
+        convolutionOp.release();
+#endif
     }
 
     bool setActivation(const Ptr<ActivationLayer>& layer)

modules/dnn/src/layers/fully_connected_layer.cpp

@@ -267,6 +267,11 @@ class FullyConnectedLayerImpl : public InnerProductLayer
     };
 
 #ifdef HAVE_OPENCL
+    void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
+    {
+        innerProductOp.release();
+    }
+
     bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, InputArrayOfArrays internals)
     {
         std::vector<UMat> inputs;

modules/dnn/src/layers/lrn_layer.cpp

@@ -96,6 +96,11 @@ class LRNLayerImpl : public LRNLayer
     }
 
 #ifdef HAVE_OPENCL
+    void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
+    {
+        lrnOp.release();
+    }
+
     bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
     {
         std::vector<UMat> inputs;

modules/dnn/src/layers/pooling_layer.cpp

@@ -126,6 +126,10 @@ class PoolingLayerImpl : public PoolingLayer
         }
 
         getConvPoolPaddings(inp, out, kernel, stride, padMode, Size(1, 1), pad);
+
+#ifdef HAVE_OPENCL
+        poolOp.release();
+#endif
     }
 
     virtual bool supportBackend(int backendId)

modules/dnn/src/layers/softmax_layer.cpp

@@ -95,14 +95,7 @@ class SoftMaxLayerImpl : public SoftmaxLayer
 #ifdef HAVE_OPENCL
     virtual void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
     {
-        OCL4DNNSoftmaxConfig config;
-
-        config.in_shape = shape(*inputs[0]);
-        config.axis = axisRaw;
-        config.channels = inputs[0]->size[axisRaw];
-        config.logsoftmax = logSoftMax;
-
-        softmaxOp = Ptr<OCL4DNNSoftmax<float> >(new OCL4DNNSoftmax<float>(config));
+        softmaxOp.release();
     }
 
     bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays itns)
@@ -115,6 +108,18 @@ class SoftMaxLayerImpl : public SoftmaxLayer
         outs.getUMatVector(outputs);
         itns.getUMatVector(internals);
 
+        if (softmaxOp.empty())
+        {
+            OCL4DNNSoftmaxConfig config;
+
+            config.in_shape = shape(inputs[0]);
+            config.axis = axisRaw;
+            config.channels = inputs[0].size[axisRaw];
+            config.logsoftmax = logSoftMax;
+
+            softmaxOp = Ptr<OCL4DNNSoftmax<float> >(new OCL4DNNSoftmax<float>(config));
+        }
+
         UMat& src = inputs[0];
         UMat& dstMat = outputs[0];
 

modules/dnn/test/test_googlenet.cpp

@@ -77,6 +77,10 @@ OCL_TEST(Reproducibility_GoogLeNet, Accuracy)
     net.setPreferableBackend(DNN_BACKEND_DEFAULT);
     net.setPreferableTarget(DNN_TARGET_OPENCL);
 
+    // Initialize network for a single image in the batch but test with batch size=2.
+    net.setInput(blobFromImage(Mat(224, 224, CV_8UC3)));
+    net.forward();
+
     std::vector<Mat> inpMats;
     inpMats.push_back( imread(_tf("googlenet_0.png")) );
     inpMats.push_back( imread(_tf("googlenet_1.png")) );