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https://github.com/opencv/opencv.git
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Merge pull request #10717 from pengli:dnn
This commit is contained in:
Alexander Alekhin
commit
9698b93d10
@ -144,7 +144,7 @@ public:
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UMat src = inputs[ii].reshape(1, s.size(), &s[0]);
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UMat dst = outputs[ii].reshape(1, s.size(), &s[0]);
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int number = (s[1] % 8 == 0) ? 8 : ((s[1] % 4 == 0) ? 4 : 1);
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String buildopt = format("-DNUM=%d ", number);
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String buildopt = format("-DNUM=%d", number);
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String kname = format("batch_norm%d", number);
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ocl::Kernel kernel(kname.c_str(), ocl::dnn::batchnorm_oclsrc, buildopt);
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if (kernel.empty())
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@ -43,6 +43,7 @@
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#include "../precomp.hpp"
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#include "layers_common.hpp"
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#include "op_halide.hpp"
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#include "opencl_kernels_dnn.hpp"
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namespace cv
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{
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@ -271,22 +272,47 @@ public:
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switch (op)
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{
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case SUM:
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if (coeffs.empty())
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{
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add(inputs[0], inputs[1], outputs[0]);
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for (int i = 2; i < inputs.size(); ++i)
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add(outputs[0], inputs[i], outputs[0]);
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}
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else
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{
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UMat mul0, mul1;
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multiply(coeffs[0], inputs[0], mul0);
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multiply(coeffs[1], inputs[1], mul1);
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add(mul0, mul1, outputs[0]);
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for (int i = 2; i < inputs.size(); ++i)
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int channels = total(shape(outputs[0]), 0, 2);
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int plane_size = total(shape(outputs[0]), 2);
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if (channels % 4 == 0 && plane_size % 4 == 0)
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{
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multiply(coeffs[i], inputs[i], mul0);
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add(mul0, outputs[0], outputs[0]);
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size_t localsize[] = { 128 };
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size_t globalsize[] = { (size_t)channels / 4 * localsize[0] };
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for (int i = 0; i < (inputs.size() - 1); ++i)
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{
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String buildopt = format("-DLOOP=%d", i);
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ocl::Kernel kernel("op_sum4", ocl::dnn::eltwise_oclsrc, buildopt);
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int idx = 0;
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UMat inpMat = (i == 0) ? inputs[0] : UMat();
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float coeff1 = (coeffs.empty() || i > 0) ? 1.0f : coeffs[i];
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float coeff2 = coeffs.empty() ? 1.0f : coeffs[i + 1];
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kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inputs[0]));
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kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inputs[1]));
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kernel.set(idx++, (int)plane_size);
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kernel.set(idx++, (float)coeff1);
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kernel.set(idx++, (float)coeff2);
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kernel.set(idx++, ocl::KernelArg::PtrReadWrite(outputs[0]));
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bool ret = kernel.run(1, globalsize, localsize, false);
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if (!ret)
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return false;
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}
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}
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else
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{
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float coeff1 = coeffs.empty() ? 1.f : coeffs[0];
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float coeff2 = coeffs.empty() ? 1.f : coeffs[1];
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UMat mul0, mul1;
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multiply(coeff1, inputs[0], mul0);
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multiply(coeff2, inputs[1], mul1);
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add(mul0, mul1, outputs[0]);
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for (int i = 2; i < inputs.size(); ++i)
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{
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float coeff = coeffs.empty() ? 1.f : coeffs[i];
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multiply(coeff, inputs[i], mul0);
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add(mul0, outputs[0], outputs[0]);
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}
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}
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}
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break;
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@ -93,6 +93,67 @@ public:
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}
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#ifdef HAVE_OPENCL
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bool fast_forward_ocl(std::vector<UMat> &inputs, std::vector<UMat> &outputs)
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{
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if( fuse_batch_norm && scale.empty())
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{
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bnorm->getScaleShift(scale, shift);
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bnorm_weight = scale.getUMat(ACCESS_READ);
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bnorm_bias = shift.getUMat(ACCESS_READ);
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}
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int splitDim = (acrossChannels) ? 1 : 2;
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for (size_t inpIdx = 0; inpIdx < inputs.size(); inpIdx++)
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{
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UMat &inpMat = inputs[inpIdx];
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UMat &outMat = outputs[inpIdx];
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int newRows = total(shape(inpMat), 0, splitDim);
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MatShape s = shape(newRows, inpMat.total() / newRows);
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UMat oneMat = UMat::ones(s[1], 1, CV_32F);
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UMat meanMat = UMat(s[0], 1, CV_32F);
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UMat tmpMat = UMat(s[0], s[1], CV_32F);
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float alpha = 1.0f / s[1];
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String buildopt = "-DNUM=4";
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ocl::Kernel k("mean_fuse4", ocl::dnn::mvn_oclsrc, buildopt);
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size_t localsize[] = { 128 };
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size_t globalsize[] = { (size_t)s[0] / 4 * localsize[0] };
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int argId = 0;
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k.set(argId++, ocl::KernelArg::PtrReadOnly(inpMat));
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k.set(argId++, (int)s[1]);
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k.set(argId++, alpha);
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k.set(argId++, ocl::KernelArg::PtrWriteOnly(meanMat));
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k.set(argId++, ocl::KernelArg::PtrWriteOnly(tmpMat));
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k.set(argId++, NULL, localsize[0] * sizeof(cl_float4));
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bool ret = k.run(1, globalsize, localsize, false);
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if (!ret)
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return false;
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buildopt += format(" %s %s", (fuse_batch_norm) ? "-DFUSE_BATCH_NORM" : "",
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(fuse_relu) ? "-DFUSE_RELU" : "");
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ocl::Kernel k1("mvn_fuse4", ocl::dnn::mvn_oclsrc, buildopt);
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argId = 0;
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k1.set(argId++, ocl::KernelArg::PtrReadOnly(tmpMat));
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k1.set(argId++, ocl::KernelArg::PtrReadOnly(inpMat));
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k1.set(argId++, ocl::KernelArg::PtrReadOnly(meanMat));
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k1.set(argId++, (int)s[1]);
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k1.set(argId++, (float)alpha);
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k1.set(argId++, (float)eps);
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k1.set(argId++, (float)relu_slope);
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k1.set(argId++, ocl::KernelArg::PtrReadOnly(bnorm_weight));
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k1.set(argId++, ocl::KernelArg::PtrReadOnly(bnorm_bias));
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k1.set(argId++, ocl::KernelArg::PtrWriteOnly(outMat));
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k1.set(argId++, NULL, localsize[0] * sizeof(cl_float4));
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ret = k1.run(1, globalsize, localsize, false);
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if (!ret)
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return false;
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}
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return true;
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}
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bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
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{
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std::vector<UMat> inputs;
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@ -101,6 +162,15 @@ public:
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inputs_.getUMatVector(inputs);
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outputs_.getUMatVector(outputs);
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int splitDim = (acrossChannels) ? 1 : 2;
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int row_size = total(shape(inputs[0]), 0, splitDim);
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int plane_size = total(shape(inputs[0]), splitDim);
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if (normVariance && (row_size % 4 == 0) && (plane_size % 4 == 0))
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{
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bool ret = fast_forward_ocl(inputs, outputs);
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return ret;
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}
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if( fuse_batch_norm && scale.empty())
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{
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bnorm->getScaleShift(scale, shift);
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@ -112,11 +182,7 @@ public:
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{
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UMat &inpMat = inputs[inpIdx];
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UMat &outMat = outputs[inpIdx];
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int splitDim = (acrossChannels) ? 1 : 2;
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int i, newRows = 1;
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for( i = 0; i < splitDim; i++ )
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newRows *= inpMat.size[i];
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int newRows = total(shape(inpMat), 0, splitDim);
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MatShape s = shape(newRows, inpMat.total() / newRows);
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UMat oneMat = UMat::ones(s[1], 1, CV_32F);
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@ -43,6 +43,7 @@
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#include "../precomp.hpp"
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#include "layers_common.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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#include "opencl_kernels_dnn.hpp"
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namespace cv
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{
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@ -171,11 +172,59 @@ public:
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}
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}
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#ifdef HAVE_OPENCL
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bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
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{
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std::vector<UMat> inputs;
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std::vector<UMat> outputs;
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inputs_.getUMatVector(inputs);
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outputs_.getUMatVector(outputs);
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if (inputs[0].dims < 4)
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return false;
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const UMat& inpMat = inputs[0];
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for (size_t i = 0; i < outputs.size(); i++)
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{
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int groups = outputs[i].size[0];
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int channels = outputs[i].size[1];
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int rows = outputs[i].size[2];
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int cols = outputs[i].size[3];
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int number = (cols % 8 == 0) ? 8 : ((cols % 4 == 0) ? 4 : 1);
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String buildopt = format("-DNUM=%d ", number);
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String kname = format("slice%d", number);
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ocl::Kernel kernel(kname.c_str(), ocl::dnn::slice_oclsrc, buildopt);
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size_t global[] = { (size_t)groups * channels, (size_t)rows * cols / number };
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int idx = 0;
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kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inpMat));
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kernel.set(idx++, (int)(inpMat.size[2] * inpMat.size[3]));
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kernel.set(idx++, (int)inpMat.size[3]);
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kernel.set(idx++, (int)global[0]);
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kernel.set(idx++, (int)(rows * cols));
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kernel.set(idx++, (int)cols);
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kernel.set(idx++, (int)sliceRanges[i][2].start);
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kernel.set(idx++, (int)sliceRanges[i][3].start);
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kernel.set(idx++, ocl::KernelArg::PtrWriteOnly(outputs[i]));
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bool ret = kernel.run(2, global, NULL, false);
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if (!ret)
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return false;
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}
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return true;
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}
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#endif
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void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
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{
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CV_TRACE_FUNCTION();
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CV_TRACE_ARG_VALUE(name, "name", name.c_str());
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CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
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OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
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forward_ocl(inputs_arr, outputs_arr, internals_arr))
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Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
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}
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98
modules/dnn/src/opencl/eltwise.cl
Normal file
98
modules/dnn/src/opencl/eltwise.cl
Normal file
@ -0,0 +1,98 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2017, Intel Corporation, all rights reserved.
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||||
// Copyright (c) 2016-2017 Fabian David Tschopp, all rights reserved.
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// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
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#define Dtype float
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#define Dtype4 float4
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#define Dtype8 float8
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__kernel void op_sum4(__global const Dtype * A,
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__global const Dtype * B,
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unsigned int A_col_size,
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const float coeff1,
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const float coeff2,
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__global Dtype * C)
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{
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unsigned int row_gid = get_group_id(0);
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unsigned int lid = get_local_id(0);
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const __global Dtype *src0_read = A + row_gid * 4 * A_col_size;
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const __global Dtype *src1_read = B + row_gid * 4 * A_col_size;
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__global Dtype *dst0_read = C + row_gid * 4 * A_col_size;
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Dtype4 a0, a1, a2, a3;
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Dtype4 dot0, dot1, dot2, dot3;
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unsigned int i = lid;
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while( i < A_col_size / 4)
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{
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const Dtype4 b0 = vload4(i, src1_read);
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const Dtype4 b1 = vload4(i, src1_read + A_col_size);
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const Dtype4 b2 = vload4(i, src1_read + 2 * A_col_size);
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const Dtype4 b3 = vload4(i, src1_read + 3 * A_col_size);
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#if LOOP == 0
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a0 = vload4(i, src0_read);
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a1 = vload4(i, src0_read + A_col_size);
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a2 = vload4(i, src0_read + 2 * A_col_size);
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a3 = vload4(i, src0_read + 3 * A_col_size);
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dot0 = a0 * coeff1 + b0 * coeff2;
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dot1 = a1 * coeff1 + b1 * coeff2;
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dot2 = a2 * coeff1 + b2 * coeff2;
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dot3 = a3 * coeff1 + b3 * coeff2;
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#else
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a0 = vload4(i, dst0_read);
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a1 = vload4(i, dst0_read + A_col_size);
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a2 = vload4(i, dst0_read + 2 * A_col_size);
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a3 = vload4(i, dst0_read + 3 * A_col_size);
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dot0 = a0 + b0 * coeff2;
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dot1 = a1 + b1 * coeff2;
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dot2 = a2 + b2 * coeff2;
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dot3 = a3 + b3 * coeff2;
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#endif
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vstore4(dot0, i, dst0_read);
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vstore4(dot1, i, dst0_read + A_col_size);
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vstore4(dot2, i, dst0_read + 2 * A_col_size);
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vstore4(dot3, i, dst0_read + 3 * A_col_size);
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|
||||
i += get_local_size(0);
|
||||
}
|
||||
}
|
||||
@ -50,18 +50,24 @@
|
||||
#define vec_type Dtype8
|
||||
#define CALC_MEAN calc_mean8
|
||||
#define MVN mvn8
|
||||
#define MEAN_FUSE mean_fuse8
|
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#define MVN_FUSE mvn_fuse8
|
||||
#elif NUM == 4
|
||||
#define load(src, index) vload4(0, src + index)
|
||||
#define store(vec, dst, index) vstore4(vec, 0, dst + index)
|
||||
#define vec_type Dtype4
|
||||
#define CALC_MEAN calc_mean4
|
||||
#define MVN mvn4
|
||||
#define MEAN_FUSE mean_fuse4
|
||||
#define MVN_FUSE mvn_fuse4
|
||||
#elif NUM == 1
|
||||
#define load(src, index) src[index]
|
||||
#define store(vec, dst, index) dst[index] = vec
|
||||
#define vec_type Dtype
|
||||
#define CALC_MEAN calc_mean1
|
||||
#define MVN mvn1
|
||||
#define MEAN_FUSE mean_fuse1
|
||||
#define MVN_FUSE mvn_fuse1
|
||||
#endif
|
||||
|
||||
__kernel void CALC_MEAN(__global const Dtype* src,
|
||||
@ -128,3 +134,177 @@ __kernel void MVN(__global const Dtype* src,
|
||||
|
||||
store(dst_vec, dst, index);
|
||||
}
|
||||
|
||||
__kernel void MEAN_FUSE(__global const Dtype * A,
|
||||
unsigned int A_col_size,
|
||||
float alpha,
|
||||
__global Dtype4 * result,
|
||||
__global Dtype * B,
|
||||
__local Dtype4 * work)
|
||||
{
|
||||
unsigned int row_gid = get_group_id(0);
|
||||
unsigned int lid = get_local_id(0);
|
||||
const __global Dtype *src0_read = A + row_gid * 4 * A_col_size;
|
||||
__global Dtype *dst0_read = B + row_gid * 4 * A_col_size;
|
||||
Dtype4 dot0, dot1, dot2, dot3;
|
||||
dot0 = dot1 = dot2 = dot3 = (Dtype4)(0.f);
|
||||
|
||||
unsigned int i = lid;
|
||||
const Dtype4 b0 = (Dtype4)1.f;
|
||||
while( i < A_col_size / 4)
|
||||
{
|
||||
const Dtype4 a0 = vload4(i, src0_read);
|
||||
const Dtype4 a1 = vload4(i, src0_read + A_col_size);
|
||||
const Dtype4 a2 = vload4(i, src0_read + 2 * A_col_size);
|
||||
const Dtype4 a3 = vload4(i, src0_read + 3 * A_col_size);
|
||||
|
||||
dot0 += a0;
|
||||
dot1 += a1;
|
||||
dot2 += a2;
|
||||
dot3 += a3;
|
||||
|
||||
i += get_local_size(0);
|
||||
}
|
||||
|
||||
work[lid].s0 = dot(dot0, b0);
|
||||
work[lid].s1 = dot(dot1, b0);
|
||||
work[lid].s2 = dot(dot2, b0);
|
||||
work[lid].s3 = dot(dot3, b0);
|
||||
|
||||
for(unsigned int stride=get_local_size(0)/2 ; stride>0 ; stride>>=1)
|
||||
{
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
if(lid < stride)
|
||||
work[lid] += work[lid+stride];
|
||||
}
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
if(lid == 0)
|
||||
{
|
||||
result[row_gid] = alpha * work[0];
|
||||
}
|
||||
|
||||
Dtype4 sum = work[0] * alpha;
|
||||
i = lid;
|
||||
while( i < A_col_size / 4)
|
||||
{
|
||||
const Dtype4 a0 = vload4(i, src0_read);
|
||||
const Dtype4 a1 = vload4(i, src0_read + A_col_size);
|
||||
const Dtype4 a2 = vload4(i, src0_read + 2 * A_col_size);
|
||||
const Dtype4 a3 = vload4(i, src0_read + 3 * A_col_size);
|
||||
|
||||
dot0 = native_powr(a0 - (Dtype4)sum.x, 2);
|
||||
dot1 = native_powr(a1 - (Dtype4)sum.y, 2);
|
||||
dot2 = native_powr(a2 - (Dtype4)sum.z, 2);
|
||||
dot3 = native_powr(a3 - (Dtype4)sum.w, 2);
|
||||
|
||||
vstore4(dot0, i, dst0_read);
|
||||
vstore4(dot1, i, dst0_read + A_col_size);
|
||||
vstore4(dot2, i, dst0_read + 2 * A_col_size);
|
||||
vstore4(dot3, i, dst0_read + 3 * A_col_size);
|
||||
|
||||
i += get_local_size(0);
|
||||
}
|
||||
}
|
||||
|
||||
__kernel void MVN_FUSE(__global const Dtype * tmp,
|
||||
__global const Dtype * A,
|
||||
__global const Dtype4 * mean,
|
||||
unsigned int A_col_size,
|
||||
const float alpha_val,
|
||||
const float eps,
|
||||
const float relu_slope,
|
||||
__global const Dtype4 * bnorm_weight,
|
||||
__global const Dtype4 * bnorm_bias,
|
||||
__global Dtype * B,
|
||||
__local Dtype4 * work)
|
||||
{
|
||||
unsigned int row_gid = get_group_id(0);
|
||||
unsigned int lid = get_local_id(0);
|
||||
const __global Dtype *src0_read = tmp + row_gid * 4 * A_col_size;
|
||||
const __global Dtype *src1_read = A + row_gid * 4 * A_col_size;
|
||||
__global Dtype *dst0_read = B + row_gid * 4 * A_col_size;
|
||||
Dtype4 dot0, dot1, dot2, dot3;
|
||||
dot0 = dot1 = dot2 = dot3 = (Dtype4)(0.f);
|
||||
|
||||
unsigned int i = lid;
|
||||
const Dtype4 b0 = (Dtype4)1.f;
|
||||
while( i < A_col_size / 4)
|
||||
{
|
||||
const Dtype4 a0 = vload4(i, src0_read);
|
||||
const Dtype4 a1 = vload4(i, src0_read + A_col_size);
|
||||
const Dtype4 a2 = vload4(i, src0_read + 2 * A_col_size);
|
||||
const Dtype4 a3 = vload4(i, src0_read + 3 * A_col_size);
|
||||
|
||||
dot0 += a0;
|
||||
dot1 += a1;
|
||||
dot2 += a2;
|
||||
dot3 += a3;
|
||||
|
||||
i += get_local_size(0);
|
||||
}
|
||||
|
||||
work[lid].s0 = dot(dot0, b0);
|
||||
work[lid].s1 = dot(dot1, b0);
|
||||
work[lid].s2 = dot(dot2, b0);
|
||||
work[lid].s3 = dot(dot3, b0);
|
||||
|
||||
for(unsigned int stride=get_local_size(0)/2 ; stride>0 ; stride>>=1)
|
||||
{
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
if(lid < stride)
|
||||
work[lid] += work[lid+stride];
|
||||
}
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
Dtype4 mean_val = mean[row_gid];
|
||||
Dtype4 dev_val = sqrt(work[0] * alpha_val) + (Dtype4)eps;
|
||||
Dtype4 alpha = (Dtype4)1.f / dev_val;
|
||||
|
||||
Dtype4 w = (Dtype4)1.f;
|
||||
Dtype4 b = (Dtype4)0.f;
|
||||
#ifdef FUSE_BATCH_NORM
|
||||
w = bnorm_weight[row_gid];
|
||||
b = bnorm_bias[row_gid];
|
||||
#endif
|
||||
|
||||
i = lid;
|
||||
while( i < A_col_size / 4)
|
||||
{
|
||||
const Dtype4 a0 = vload4(i, src1_read);
|
||||
const Dtype4 a1 = vload4(i, src1_read + A_col_size);
|
||||
const Dtype4 a2 = vload4(i, src1_read + 2 * A_col_size);
|
||||
const Dtype4 a3 = vload4(i, src1_read + 3 * A_col_size);
|
||||
|
||||
dot0 = (a0 - (Dtype4)mean_val.x) * alpha.x;
|
||||
dot1 = (a1 - (Dtype4)mean_val.y) * alpha.y;
|
||||
dot2 = (a2 - (Dtype4)mean_val.z) * alpha.z;
|
||||
dot3 = (a3 - (Dtype4)mean_val.w) * alpha.w;
|
||||
|
||||
dot0 = dot0 * w.x + (Dtype4)b.x;
|
||||
dot1 = dot1 * w.y + (Dtype4)b.y;
|
||||
dot2 = dot2 * w.z + (Dtype4)b.z;
|
||||
dot3 = dot3 * w.w + (Dtype4)b.w;
|
||||
|
||||
#ifdef FUSE_RELU
|
||||
Dtype4 new0 = dot0 * relu_slope;
|
||||
dot0 = select(new0, dot0, dot0 > (Dtype4)0.f);
|
||||
|
||||
Dtype4 new1 = dot1 * relu_slope;
|
||||
dot1 = select(new1, dot1, dot1 > (Dtype4)0.f);
|
||||
|
||||
Dtype4 new2 = dot2 * relu_slope;
|
||||
dot2 = select(new2, dot2, dot2 > (Dtype4)0.f);
|
||||
|
||||
Dtype4 new3 = dot3 * relu_slope;
|
||||
dot3 = select(new3, dot3, dot3 > (Dtype4)0.f);
|
||||
#endif
|
||||
|
||||
vstore4(dot0, i, dst0_read);
|
||||
vstore4(dot1, i, dst0_read + A_col_size);
|
||||
vstore4(dot2, i, dst0_read + 2 * A_col_size);
|
||||
vstore4(dot3, i, dst0_read + 3 * A_col_size);
|
||||
|
||||
i += get_local_size(0);
|
||||
}
|
||||
}
|
||||
|
||||
87
modules/dnn/src/opencl/slice.cl
Normal file
87
modules/dnn/src/opencl/slice.cl
Normal file
@ -0,0 +1,87 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2017, Intel Corporation, all rights reserved.
|
||||
// Copyright (c) 2016-2017 Fabian David Tschopp, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#define Dtype float
|
||||
#define Dtype4 float4
|
||||
#define Dtype8 float8
|
||||
|
||||
#if NUM == 8
|
||||
#define load(src, index) vload8(0, src + index)
|
||||
#define store(vec, dst, index) vstore8(vec, 0, dst + index)
|
||||
#define vec_type Dtype8
|
||||
#define SLICE slice8
|
||||
#elif NUM == 4
|
||||
#define load(src, index) vload4(0, src + index)
|
||||
#define store(vec, dst, index) vstore4(vec, 0, dst + index)
|
||||
#define vec_type Dtype4
|
||||
#define SLICE slice4
|
||||
#elif NUM == 1
|
||||
#define load(src, index) src[index]
|
||||
#define store(vec, dst, index) dst[index] = vec
|
||||
#define vec_type Dtype
|
||||
#define SLICE slice1
|
||||
#endif
|
||||
|
||||
__kernel void SLICE(__global const Dtype* src,
|
||||
const int src_plane_size,
|
||||
const int src_cols,
|
||||
const int channels,
|
||||
const int dst_plane_size,
|
||||
const int dst_cols,
|
||||
const int row_offset,
|
||||
const int col_offset,
|
||||
__global Dtype* dst)
|
||||
{
|
||||
int x = get_global_id(0);
|
||||
int y = get_global_id(1) * NUM;
|
||||
|
||||
if ((x >= channels) || (y >= dst_plane_size))
|
||||
return;
|
||||
|
||||
int row = y / dst_cols + row_offset;
|
||||
int col = y % dst_cols + col_offset;
|
||||
|
||||
int src_index = x * src_plane_size + row * src_cols + col;
|
||||
int dst_index = x * dst_plane_size + y;
|
||||
vec_type val = load(src, src_index);
|
||||
store(val, dst, dst_index);
|
||||
}
|
||||
@ -367,11 +367,14 @@ OCL_TEST(Layer_Test_PReLU, Accuracy)
|
||||
// );
|
||||
//}
|
||||
|
||||
static void test_Reshape_Split_Slice_layers()
|
||||
static void test_Reshape_Split_Slice_layers(int targetId)
|
||||
{
|
||||
Net net = readNetFromCaffe(_tf("reshape_and_slice_routines.prototxt"));
|
||||
ASSERT_FALSE(net.empty());
|
||||
|
||||
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
|
||||
net.setPreferableTarget(targetId);
|
||||
|
||||
Mat input(6, 12, CV_32F);
|
||||
RNG rng(0);
|
||||
rng.fill(input, RNG::UNIFORM, -1, 1);
|
||||
@ -384,7 +387,12 @@ static void test_Reshape_Split_Slice_layers()
|
||||
|
||||
TEST(Layer_Test_Reshape_Split_Slice, Accuracy)
|
||||
{
|
||||
test_Reshape_Split_Slice_layers();
|
||||
test_Reshape_Split_Slice_layers(DNN_TARGET_CPU);
|
||||
}
|
||||
|
||||
OCL_TEST(Layer_Test_Reshape_Split_Slice, Accuracy)
|
||||
{
|
||||
test_Reshape_Split_Slice_layers(DNN_TARGET_OPENCL);
|
||||
}
|
||||
|
||||
TEST(Layer_Conv_Elu, Accuracy)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user