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dnn(ocl4dnn): don't use getUMat()

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especially in CPU only processing
This commit is contained in:
Alexander Alekhin
2018-10-05 15:10:58 +03:00
parent 0101fa789c
commit 9d02d42afe
7 changed files with 48 additions and 20 deletions
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modules/dnn/src/layers/fully_connected_layer.cpp
+8 -8
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@@ -96,12 +96,6 @@ public:
biasMat = blobs[1] = blobs[1].reshape(1, 1);
else
biasMat = Mat::zeros(1, numOutput, weightsMat.type());
#ifdef HAVE_OPENCL
size_t n = blobs.size();
umat_blobs.resize(n);
for (int i = 0; i < n; i++) umat_blobs[i] = blobs[i].getUMat(ACCESS_READ);
#endif
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -276,6 +270,8 @@ public:
virtual void finalize(InputArrayOfArrays, OutputArrayOfArrays) CV_OVERRIDE
{
innerProductOp.release();
umat_blobs.clear();
half_blobs.clear();
}
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, InputArrayOfArrays internals)
@@ -288,13 +284,17 @@ public:
outs.getUMatVector(outputs);
int axisCan = clamp(axis, inputs[0].dims);
int numOutput = umat_blobs[0].size[0];
int innerSize = umat_blobs[0].size[1];
int numOutput = blobs[0].size[0];
int innerSize = blobs[0].size[1];
int outerSize = total(shape(inputs[0]), 0, axisCan);
bool ret = true;
if (innerProductOp.empty())
{
size_t n = blobs.size();
umat_blobs.resize(n);
for (int i = 0; i < n; i++) blobs[i].copyTo(umat_blobs[i]);
OCL4DNNInnerProductConfig config;
config.num_output = numOutput;
config.bias_term = bias;