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make ocl4dnn available to run on other platform than Intel GPU

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This commit is contained in:
Tomoaki Teshima
2018-05-29 19:18:10 +09:00
parent faafb3152a
commit 2e9e71ab9e
6 changed files with 55 additions and 24 deletions
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modules/dnn/src/layers/mvn_layer.cpp
+39 -12
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@@ -73,7 +73,7 @@ public:
virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
{
if (preferableTarget == DNN_TARGET_OPENCL && !fuse_batch_norm)
if (!fuse_batch_norm)
{
top->getScaleShift(scale, shift);
fuse_batch_norm = !scale.empty() || !shift.empty();
@@ -252,8 +252,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
@@ -274,25 +273,53 @@ public:
for( i = 0; i < splitDim; i++ )
newRows *= inpBlob.size[i];
if (inpBlob.total() == newRows)
{
// MVN is applied to single values at an every row.
outBlob.setTo(0);
return;
}
Mat inpMat = inpBlob.reshape(1, newRows);
Mat outMat = outBlob.reshape(1, newRows);
if ( inpBlob.total() == newRows )
{
// MVN is applied to single values at an every row.
if (shift.empty())
{
outBlob.setTo(0);
}
else
{
for ( i = 0; i < newRows; i++ )
{
outMat.row(i).setTo(((float*)shift.data)[i]);
}
}
return;
}
Scalar mean, dev;
for ( i = 0; i < newRows; i++)
{
Mat inpRow = inpMat.row(i);
Mat outRow = outMat.row(i);
float weight = 1.f;
float bias = 0.f;
if (fuse_batch_norm)
{
weight = i < scale.cols ? ((float*)scale.data)[i] : weight;
bias = i < shift.cols ? ((float*)shift.data)[i] : bias;
}
cv::meanStdDev(inpRow, mean, (normVariance) ? dev : noArray());
double alpha = (normVariance) ? 1/(eps + dev[0]) : 1;
inpRow.convertTo(outRow, outRow.type(), alpha, -mean[0] * alpha);
double normalizationScale = 1.0;
double normalizationShift = 0.0;
if (fuse_batch_norm)
{
normalizationScale = alpha * weight;
normalizationShift = -mean[0] * normalizationScale + bias;
}
else
{
normalizationScale = alpha;
normalizationShift = -mean[0] * alpha;
}
inpRow.convertTo(outRow, outRow.type(), normalizationScale, normalizationShift);
}
}
}