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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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3
modules/dnn/src/layers/convolution_layer.cpp
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@ -966,8 +966,7 @@ public:
CV_TRACE_FUNCTION(); CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str()); CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) && CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr)) forward_ocl(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr); Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);

3
modules/dnn/src/layers/elementwise_layers.cpp
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@ -176,8 +176,7 @@ public:
{ {
CV_TRACE_FUNCTION(); CV_TRACE_FUNCTION();
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(this->preferableTarget) && CV_OCL_RUN(IS_DNN_OPENCL_TARGET(this->preferableTarget),
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
func.applyOCL(inputs_arr, outputs_arr, internals_arr)) func.applyOCL(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr); Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);

51
modules/dnn/src/layers/mvn_layer.cpp
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@ -73,7 +73,7 @@ public:
virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
{ {
if (preferableTarget == DNN_TARGET_OPENCL && !fuse_batch_norm) if (!fuse_batch_norm)
{ {
top->getScaleShift(scale, shift); top->getScaleShift(scale, shift);
fuse_batch_norm = !scale.empty() || !shift.empty(); fuse_batch_norm = !scale.empty() || !shift.empty();
@ -252,8 +252,7 @@ public:
CV_TRACE_FUNCTION(); CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str()); CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) && CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr)) forward_ocl(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(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++ ) for( i = 0; i < splitDim; i++ )
newRows *= inpBlob.size[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 inpMat = inpBlob.reshape(1, newRows);
Mat outMat = outBlob.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; Scalar mean, dev;
for ( i = 0; i < newRows; i++) for ( i = 0; i < newRows; i++)
{ {
Mat inpRow = inpMat.row(i); Mat inpRow = inpMat.row(i);
Mat outRow = outMat.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()); cv::meanStdDev(inpRow, mean, (normVariance) ? dev : noArray());
double alpha = (normVariance) ? 1/(eps + dev[0]) : 1; 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);
} }
} }
} }

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

@ -191,8 +191,7 @@ public:
CV_TRACE_FUNCTION(); CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str()); CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) && CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr)) forward_ocl(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr); Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);

15
modules/dnn/src/opencl/mvn.cl
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@ -89,7 +89,8 @@ __kernel void CALC_MEAN(__global const Dtype* src,
Dtype mean_val = mean[x]; Dtype mean_val = mean[x];
vec_type src_vec = load(src, index); vec_type src_vec = load(src, index);
vec_type dst_vec = native_powr(src_vec - (vec_type)mean_val, 2); vec_type dst_vec = src_vec - (vec_type)mean_val;
dst_vec = dst_vec * dst_vec;
store(dst_vec, dst, index); store(dst_vec, dst, index);
} }
@ -197,10 +198,14 @@ __kernel void MEAN_FUSE(__global const T * A,
const T4 a2 = vload4(i, src0_read + 2 * A_col_size); const T4 a2 = vload4(i, src0_read + 2 * A_col_size);
const T4 a3 = vload4(i, src0_read + 3 * A_col_size); const T4 a3 = vload4(i, src0_read + 3 * A_col_size);
dot0 = native_powr(convert_float4(a0) - (Dtype4)sum.x, 2); dot0 = convert_float4(a0) - (Dtype4)sum.x;
dot1 = native_powr(convert_float4(a1) - (Dtype4)sum.y, 2); dot1 = convert_float4(a1) - (Dtype4)sum.y;
dot2 = native_powr(convert_float4(a2) - (Dtype4)sum.z, 2); dot2 = convert_float4(a2) - (Dtype4)sum.z;
dot3 = native_powr(convert_float4(a3) - (Dtype4)sum.w, 2); dot3 = convert_float4(a3) - (Dtype4)sum.w;
dot0 = dot0 * dot0;
dot1 = dot1 * dot1;
dot2 = dot2 * dot2;
dot3 = dot3 * dot3;
vstore4(dot0, i, dst0_read); vstore4(dot0, i, dst0_read);
vstore4(dot1, i, dst0_read + A_col_size); vstore4(dot1, i, dst0_read + A_col_size);

4
modules/dnn/test/test_tf_importer.cpp
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@ -160,10 +160,12 @@ TEST_P(Test_TensorFlow_layers, batch_norm)
TEST_P(Test_TensorFlow_layers, pooling) TEST_P(Test_TensorFlow_layers, pooling)
{ {
int targetId = GetParam(); int targetId = GetParam();
cv::ocl::Device d = cv::ocl::Device::getDefault();
bool loosenFlag = targetId == DNN_TARGET_OPENCL && d.isIntel() && d.type() == cv::ocl::Device::TYPE_CPU;
runTensorFlowNet("max_pool_even", targetId); runTensorFlowNet("max_pool_even", targetId);
runTensorFlowNet("max_pool_odd_valid", targetId); runTensorFlowNet("max_pool_odd_valid", targetId);
runTensorFlowNet("ave_pool_same", targetId); runTensorFlowNet("ave_pool_same", targetId);
runTensorFlowNet("max_pool_odd_same", targetId); runTensorFlowNet("max_pool_odd_same", targetId, false, loosenFlag ? 3e-5 : 1e-5, loosenFlag ? 3e-4 : 1e-4);
runTensorFlowNet("reduce_mean", targetId); // an average pooling over all spatial dimensions. runTensorFlowNet("reduce_mean", targetId); // an average pooling over all spatial dimensions.
} }