mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2025-06-11 20:09:23 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Reset OpenCL kernels if batch size changes

Browse Source
This commit is contained in:
Dmitry Kurtaev 2018-03-07 16:56:41 +03:00
parent 0a61ebdd66
commit 0f01b40dd5
6 changed files with 34 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
modules/dnn/src/layers/convolution_layer.cpp
View File

@ -273,6 +273,9 @@ public:
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)

5
modules/dnn/src/layers/fully_connected_layer.cpp
View File

@ -267,6 +267,11 @@ public:
};
#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;

5
modules/dnn/src/layers/lrn_layer.cpp
View File

@ -96,6 +96,11 @@ public:
}
#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;

4
modules/dnn/src/layers/pooling_layer.cpp
View File

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

21
modules/dnn/src/layers/softmax_layer.cpp
View File

@ -95,14 +95,7 @@ public:
#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 @@ public:
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];

4
modules/dnn/test/test_googlenet.cpp
View File

@ -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")) );