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279 lines
9.4 KiB
C++
279 lines
9.4 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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// Copyright (C) 2016, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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/*
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Implementation of Batch Normalization layer.
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*/
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#include "../precomp.hpp"
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#include "op_halide.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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namespace dnn
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{
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class BatchNormLayerImpl : public BatchNormLayer
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{
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public:
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Mat weights_, bias_;
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UMat umat_weight, umat_bias;
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BatchNormLayerImpl(const LayerParams& params)
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{
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setParamsFrom(params);
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CV_Assert(blobs.size() >= 3);
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hasWeights = params.get<bool>("has_weight", false);
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hasBias = params.get<bool>("has_bias", false);
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epsilon = params.get<float>("eps", 1E-5);
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size_t n = blobs[0].total();
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CV_Assert(blobs[1].total() == n &&
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blobs[0].isContinuous() && blobs[1].isContinuous() &&
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blobs[0].type() == CV_32F && blobs[1].type() == CV_32F);
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float varMeanScale = 1.f;
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if (!hasWeights && !hasBias) {
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CV_Assert(blobs[2].type() == CV_32F);
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varMeanScale = blobs[2].at<float>(0);
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if (varMeanScale != 0)
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varMeanScale = 1/varMeanScale;
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}
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const int weightsBlobIndex = 2;
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const int biasBlobIndex = weightsBlobIndex + hasWeights;
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if( hasWeights )
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{
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CV_Assert((size_t)weightsBlobIndex < blobs.size());
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const Mat& w = blobs[weightsBlobIndex];
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CV_Assert(w.isContinuous() && w.type() == CV_32F && w.total() == (size_t)n);
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}
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if( hasBias )
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{
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CV_Assert((size_t)biasBlobIndex < blobs.size());
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const Mat& b = blobs[weightsBlobIndex];
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CV_Assert(b.isContinuous() && b.type() == CV_32F && b.total() == (size_t)n);
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}
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const float* meanData = blobs[0].ptr<float>();
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const float* stdData = blobs[1].ptr<float>();
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const float* weightsData = hasWeights ? blobs[weightsBlobIndex].ptr<float>() : 0;
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const float* biasData = hasBias ? blobs[biasBlobIndex].ptr<float>() : 0;
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weights_.create(1, (int)n, CV_32F);
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bias_.create(1, (int)n, CV_32F);
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float* dstWeightsData = weights_.ptr<float>();
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float* dstBiasData = bias_.ptr<float>();
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for (size_t i = 0; i < n; ++i)
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{
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float w = (hasWeights ? weightsData[i] : 1.0f) / sqrt(stdData[i] * varMeanScale + epsilon);
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dstWeightsData[i] = w;
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dstBiasData[i] = (hasBias ? biasData[i] : 0.0f) - w * meanData[i] * varMeanScale;
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}
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}
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void getScaleShift(Mat& scale, Mat& shift) const
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{
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scale = weights_;
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shift = bias_;
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}
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bool getMemoryShapes(const std::vector<MatShape> &inputs,
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const int requiredOutputs,
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std::vector<MatShape> &outputs,
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std::vector<MatShape> &internals) const
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{
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Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
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return true;
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}
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virtual bool supportBackend(int backendId)
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{
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return backendId == DNN_BACKEND_DEFAULT ||
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backendId == DNN_BACKEND_HALIDE && haveHalide();
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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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CV_Assert(blobs.size() >= 2);
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CV_Assert(inputs.size() == 1);
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if (umat_weight.empty())
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{
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umat_weight = weights_.getUMat(ACCESS_READ);
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umat_bias = bias_.getUMat(ACCESS_READ);
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}
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UMat &inpBlob = inputs[0];
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CV_Assert(inpBlob.dims == 2 || inpBlob.dims == 4);
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int groups = inpBlob.size[0];
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int channels = inpBlob.size[1];
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int rows = inpBlob.dims > 2 ? inpBlob.size[2] : 1;
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int cols = inpBlob.dims > 2 ? inpBlob.size[3] : 1;
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for (size_t ii = 0; ii < outputs.size(); ii++)
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{
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if (inpBlob.dims == 2)
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{
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UMat& src = inputs[ii];
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UMat& dst = outputs[ii];
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multiply(src, weights_, dst);
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add(dst, bias_, dst);
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}
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else
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{
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MatShape s = shape(groups * channels, rows * cols);
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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 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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return false;
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size_t global[] = { (size_t)s[0], (size_t)(s[1] / number) };
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kernel.set(0, ocl::KernelArg::PtrReadOnly(src));
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kernel.set(1, (int)s[0]);
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kernel.set(2, (int)s[1]);
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kernel.set(3, (int)channels);
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kernel.set(4, ocl::KernelArg::PtrReadOnly(umat_weight));
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kernel.set(5, ocl::KernelArg::PtrReadOnly(umat_bias));
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kernel.set(6, ocl::KernelArg::PtrWriteOnly(dst));
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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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}
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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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void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
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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_Assert(blobs.size() >= 2);
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CV_Assert(inputs.size() == 1);
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Mat &inpBlob = *inputs[0];
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CV_Assert(inpBlob.dims == 2 || inpBlob.dims == 4);
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int rows = inpBlob.dims > 2 ? inpBlob.size[2] : 1;
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int cols = inpBlob.dims > 2 ? inpBlob.size[3] : 1;
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for (size_t ii = 0; ii < outputs.size(); ii++)
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{
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Mat &outBlob = outputs[ii];
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for(int num = 0; num < outBlob.size[0]; num++)
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{
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for (int n = 0; n < outBlob.size[1]; n++)
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{
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float w = weights_.at<float>(n);
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float b = bias_.at<float>(n);
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Mat inpBlobPlane(rows, cols, CV_32F, inpBlob.ptr<float>(num, n));
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Mat outBlobPlane(rows, cols, CV_32F, outBlob.ptr<float>(num, n));
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inpBlobPlane.convertTo(outBlobPlane, CV_32F, w, b);
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}
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}
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}
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}
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virtual Ptr<BackendNode> tryAttach(const Ptr<BackendNode>& node)
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{
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switch (node->backendId)
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{
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case DNN_BACKEND_HALIDE:
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{
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#ifdef HAVE_HALIDE
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auto base = node.dynamicCast<HalideBackendNode>();
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Halide::Func& input = base->funcs.back();
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Halide::Var x("x"), y("y"), c("c"), n("n");
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Halide::Func top = attachHalide(input(x, y, c, n));
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return Ptr<BackendNode>(new HalideBackendNode(base, top));
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#endif // HAVE_HALIDE
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break;
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}
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}
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return Ptr<BackendNode>();
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}
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virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
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{
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#ifdef HAVE_HALIDE
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Halide::Buffer<float> input = halideBuffer(inputs[0]);
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Halide::Var x("x"), y("y"), c("c"), n("n");
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Halide::Func top = attachHalide(input(x, y, c, n));
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return Ptr<BackendNode>(new HalideBackendNode(top));
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#endif // HAVE_HALIDE
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return Ptr<BackendNode>();
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}
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#ifdef HAVE_HALIDE
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// attachHalide can work both with Halide::Buffer and Halide::Func. In the
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// second case it will be a fusion.
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Halide::Func attachHalide(const Halide::Expr& input)
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{
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Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
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Halide::Var x("x"), y("y"), c("c"), n("n");
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const int numChannels = weights_.total();
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auto weights = wrapToHalideBuffer(weights_, {numChannels});
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auto bias = wrapToHalideBuffer(bias_, {numChannels});
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top(x, y, c, n) = input * weights(c) + bias(c);
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return top;
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}
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#endif // HAVE_HALIDE
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virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
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const std::vector<MatShape> &outputs) const
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{
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(void)outputs; // suppress unused variable warning
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int64 flops = 0;
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for(int i = 0; i < inputs.size(); i++)
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{
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flops += 3*total(inputs[i]);
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}
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return flops;
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}
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};
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Ptr<BatchNormLayer> BatchNormLayer::create(const LayerParams& params)
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{
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return Ptr<BatchNormLayer>(new BatchNormLayerImpl(params));
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}
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} // namespace dnn
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} // namespace cv
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