/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/operators
Edit: /usr/local/lib64/python3.6/site-packages/torch/include/caffe2/operators/normalize_op.h (3013B)
#ifndef CAFFE2_OPERATORS_NORMALIZE_OP_H_
#define CAFFE2_OPERATORS_NORMALIZE_OP_H_
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/utils/eigen_utils.h"
#include "caffe2/utils/math.h"
#define KEPS 1e-12f
namespace caffe2 {
template
class NormalizeOp final : public Operator {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template
explicit NormalizeOp(Args&&... args)
: Operator(std::forward(args)...) {}
bool RunOnDevice() override {
const auto& x = Input(0);
const auto* xData = x.template data();
auto* y = Output(0, x.sizes(), at::dtype());
auto* yData = y->template mutable_data();
const auto canonical_axis = x.canonical_axis_index(
this->template GetSingleArgument("axis", -1));
const int64_t m = x.dim(canonical_axis);
const size_t n = x.numel() / m;
const size_t sf = x.size_from_dim(canonical_axis + 1);
DoNormalize(xData, yData, m, n, sf);
return true;
}
private:
const T kEps_ = KEPS;
void DoNormalize(
const T* xData,
T* yData,
const int m,
const int n,
const int sf) {
using InnerStride = Eigen::InnerStride;
using StridedVec =
Eigen::Map, 0, InnerStride>;
using ConstStridedVec =
Eigen::Map, 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));
auto norm = xVec.template lpNorm<2>();
norm = std::max(norm, kEps_);
StridedVec yVec(yData + base, 1, m, InnerStride(sf));
yVec = xVec / norm;
}
}
};
template
class NormalizeGradientOp final : public Operator {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template
explicit NormalizeGradientOp(Args&&... args)
: Operator(std::forward(args)...) {}
bool RunOnDevice() override {
const auto& x = Input(0);
const auto& gOut = Input(GRAD_OUT);
auto* gIn = Output(GRAD_IN, gOut.sizes(), at::dtype());
const auto* xData = x.template data();
const auto* gOutData = gOut.template data();
auto* gInData = gIn->template mutable_data();
const auto canonical_axis = x.canonical_axis_index(
this->template GetSingleArgument("axis", -1));
const int m = x.dim32(canonical_axis);
const int n = x.numel() / m;
const int sf = x.size_from_dim(canonical_axis + 1);
DoNormalize(xData, gOutData, gInData, m, n, sf);
return true;
}
private:
const T kEps_ = KEPS;
void DoNormalize(
const T* xData,
const T* gOutData,
T* gInData,
const int m,
const int n,
const int sf);
INPUT_TAGS(INPUT, GRAD_OUT);
OUTPUT_TAGS(GRAD_IN);
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_NORMALIZE_OP_H_