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#include "caffe2/operators/normalize_op.h"
#include "caffe2/core/tensor.h"
namespace caffe2 {
template <typename T, class Context>
void NormalizeGradientOp<T, Context>::DoNormalize(
const T* xData,
const T* gOutData,
T* gInData,
const int m,
const int n,
const int sf) {
using InnerStride = Eigen::InnerStride<Eigen::Dynamic>;
using StridedVec =
Eigen::Map<Eigen::Matrix<T, 1, Eigen::Dynamic>, 0, InnerStride>;
using ConstStridedVec =
Eigen::Map<const Eigen::Matrix<T, 1, Eigen::Dynamic>, 0, InnerStride>;
for (int i = 0; i < n; ++i) {
auto base = (i / sf) * sf * m + (i % sf);
ConstStridedVec xVec(xData + base, 1, m, InnerStride(sf));
ConstStridedVec gOutVec(gOutData + base, 1, m, InnerStride(sf));
auto row_sum = xVec.dot(gOutVec);
auto row_norm = xVec.template lpNorm<2>();
row_norm = std::max(row_norm, kEps_);
auto row_norm_3 = pow(row_norm, 3);
StridedVec gInVec(gInData + base, 1, m, InnerStride(sf));
gInVec = (gOutVec / row_norm) - ((xVec / row_norm_3) * row_sum);
}
};
REGISTER_CPU_OPERATOR(Normalize, NormalizeOp<float, CPUContext>);
OPERATOR_SCHEMA(Normalize)
.NumInputs(1)
.NumOutputs(1)
.Arg("axis", "axis to normalize")
.SetDoc(R"DOC(
Given a matrix, apply L2-normalization along the specified dimension.
)DOC")
.IdenticalTypeAndShape();
REGISTER_CPU_GRADIENT_OPERATOR(
NormalizeGradient,
NormalizeGradientOp<float, CPUContext>);
GRADIENT_OPERATOR_SCHEMA(NormalizeGradient)
.NumInputs(2)
.NumOutputs(1)
.Arg("axis", "axis to normalize");
class GetNormalizeGradient final : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
CAFFE_ENFORCE_EQ(def_.input_size(), 1);
return SingleGradientDef(
"NormalizeGradient",
"",
vector<string>{I(0), GO(0)},
vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(Normalize, GetNormalizeGradient);
} // namespace caffe2
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