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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2016, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
/*
Implementation of Batch Normalization layer.
*/
#include "../precomp.hpp"
#include "op_halide.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace cv
{
namespace dnn
{
class BatchNormLayerImpl : public BatchNormLayer
{
public:
Mat weights_, bias_;
BatchNormLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
CV_Assert(blobs.size() >= 3);
hasWeights = params.get<bool>("has_weight", false);
hasBias = params.get<bool>("has_bias", false);
epsilon = params.get<float>("eps", 1E-5);
size_t n = blobs[0].total();
CV_Assert(blobs[1].total() == n &&
blobs[0].isContinuous() && blobs[1].isContinuous() &&
blobs[0].type() == CV_32F && blobs[1].type() == CV_32F);
float varMeanScale = 1.f;
if (!hasWeights && !hasBias) {
CV_Assert(blobs[2].type() == CV_32F);
varMeanScale = blobs[2].at<float>(0);
if (varMeanScale != 0)
varMeanScale = 1/varMeanScale;
}
const int weightsBlobIndex = 2;
const int biasBlobIndex = weightsBlobIndex + hasWeights;
if( hasWeights )
{
CV_Assert((size_t)weightsBlobIndex < blobs.size());
const Mat& w = blobs[weightsBlobIndex];
CV_Assert(w.isContinuous() && w.type() == CV_32F && w.total() == (size_t)n);
}
if( hasBias )
{
CV_Assert((size_t)biasBlobIndex < blobs.size());
const Mat& b = blobs[weightsBlobIndex];
CV_Assert(b.isContinuous() && b.type() == CV_32F && b.total() == (size_t)n);
}
const float* meanData = blobs[0].ptr<float>();
const float* stdData = blobs[1].ptr<float>();
const float* weightsData = hasWeights ? blobs[weightsBlobIndex].ptr<float>() : 0;
const float* biasData = hasBias ? blobs[biasBlobIndex].ptr<float>() : 0;
weights_.create(1, (int)n, CV_32F);
bias_.create(1, (int)n, CV_32F);
float* dstWeightsData = weights_.ptr<float>();
float* dstBiasData = bias_.ptr<float>();
for (size_t i = 0; i < n; ++i)
{
float w = (hasWeights ? weightsData[i] : 1.0f) / sqrt(stdData[i] * varMeanScale + epsilon);
dstWeightsData[i] = w;
dstBiasData[i] = (hasBias ? biasData[i] : 0.0f) - w * meanData[i] * varMeanScale;
}
}
void getScaleShift(Mat& scale, Mat& shift) const
{
scale = weights_;
shift = bias_;
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
return true;
}
virtual bool supportBackend(int backendId)
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_HALIDE && haveHalide();
}
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_Assert(blobs.size() >= 2);
CV_Assert(inputs.size() == 1);
Mat &inpBlob = *inputs[0];
int rows = inpBlob.size[2];
int cols = inpBlob.size[3];
for (size_t ii = 0; ii < outputs.size(); ii++)
{
Mat &outBlob = outputs[ii];
for(int num = 0; num < outBlob.size[0]; num++)
{
for (int n = 0; n < outBlob.size[1]; n++)
{
float w = weights_.at<float>(n);
float b = bias_.at<float>(n);
Mat inpBlobPlane(rows, cols, CV_32F, inpBlob.ptr<float>(num, n));
Mat outBlobPlane(rows, cols, CV_32F, outBlob.ptr<float>(num, n));
inpBlobPlane.convertTo(outBlobPlane, CV_32F, w, b);
}
}
}
}
virtual Ptr<BackendNode> tryAttach(const Ptr<BackendNode>& node)
{
switch (node->backendId)
{
case DNN_BACKEND_HALIDE:
{
#ifdef HAVE_HALIDE
auto base = node.dynamicCast<HalideBackendNode>();
Halide::Func& input = base->funcs.back();
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = attachHalide(input(x, y, c, n));
return Ptr<BackendNode>(new HalideBackendNode(base, top));
#endif // HAVE_HALIDE
break;
}
}
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
{
#ifdef HAVE_HALIDE
Halide::Buffer<float> input = halideBuffer(inputs[0]);
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = attachHalide(input(x, y, c, n));
return Ptr<BackendNode>(new HalideBackendNode(top));
#endif // HAVE_HALIDE
return Ptr<BackendNode>();
}
#ifdef HAVE_HALIDE
// attachHalide can work both with Halide::Buffer and Halide::Func. In the
// second case it will be a fusion.
Halide::Func attachHalide(const Halide::Expr& input)
{
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
Halide::Var x("x"), y("y"), c("c"), n("n");
const int numChannels = weights_.total();
auto weights = wrapToHalideBuffer(weights_, {numChannels});
auto bias = wrapToHalideBuffer(bias_, {numChannels});
top(x, y, c, n) = input * weights(c) + bias(c);
return top;
}
#endif // HAVE_HALIDE
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const
{
(void)outputs; // suppress unused variable warning
int64 flops = 0;
for(int i = 0; i < inputs.size(); i++)
{
flops += 3*total(inputs[i]);
}
return flops;
}
};
Ptr<BatchNormLayer> BatchNormLayer::create(const LayerParams& params)
{
return Ptr<BatchNormLayer>(new BatchNormLayerImpl(params));
}
} // namespace dnn
} // namespace cv