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#include "caffe2/operators/relu_n_op.h"
#include <algorithm>
#include <functional>
#include <string>
#include "caffe2/utils/eigen_utils.h"
namespace caffe2 {
template <>
template <typename T>
bool ReluNFunctor<CPUContext>::
operator()(const int N, const T* X, T* Y, CPUContext* /* context */) const {
EigenVectorMap<T>(Y, N) =
ConstEigenVectorMap<T>(X, N).cwiseMax(T(0)).cwiseMin(T(n));
return true;
}
template <>
template <typename T>
bool ReluNGradientFunctor<CPUContext>::Forward(
const std::vector<int>& Y_dims,
const std::vector<int>& /* dY_dims */,
const T* Y,
const T* dY,
T* dX,
CPUContext* /* context */) const {
const int size = std::accumulate(
// NOLINTNEXTLINE(modernize-use-transparent-functors)
Y_dims.cbegin(), Y_dims.cend(), 1, std::multiplies<int>());
ConstEigenVectorArrayMap<T> Y_arr(Y, size);
EigenVectorArrayMap<T>(dX, size) =
(Y_arr > T(0) && Y_arr < T(n))
.select(ConstEigenVectorArrayMap<T>(dY, size), T(0));
return true;
}
namespace {
OpSchema::Cost CostInferenceForReluN(
const OperatorDef& def,
const vector<TensorShape>& in) {
struct OpSchema::Cost cost = PointwiseCostInference<2>(def, in);
cost.params_bytes = 0;
return cost;
}
} // namespace
REGISTER_CPU_OPERATOR(
ReluN,
UnaryElementwiseWithArgsOp<
TensorTypes<float>,
CPUContext,
ReluNFunctor<CPUContext>>);
REGISTER_CPU_OPERATOR(
ReluNGradient,
BinaryElementwiseWithArgsOp<
TensorTypes<float>,
CPUContext,
ReluNGradientFunctor<CPUContext>>);
// Input: X, output: Y
OPERATOR_SCHEMA(ReluN)
.NumInputs(1)
.NumOutputs(1)
.Arg("n", "the cap of output")
.AllowInplace({{0, 0}})
.CostInferenceFunction(CostInferenceForReluN)
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
Relu takes one input data (Tensor) and produces one output data
(Tensor) where the rectified linear function, y = min(max(0, x), n),
is applied to the tensor elementwise.
)DOC")
.Input(0, "X", "1D input tensor")
.Output(0, "Y", "1D input tensor");
// Input: Y, dY, output: dX
OPERATOR_SCHEMA(ReluNGradient)
.NumInputs(2)
.NumOutputs(1)
.Arg("n", "the cap of forward op output")
.AllowInplace({{1, 0}})
.SetDoc(R"DOC(
ReluGradient takes both Y and dY and uses this to update dX according to the
chain rule and derivatives of the rectified linear function.
)DOC");
namespace {
class GetReluNGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
std::vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
def_.type() + "Gradient",
"",
std::vector<std::string>{O(0), GO(0)},
std::vector<std::string>{GI(0)});
}
};
} // namespace
REGISTER_GRADIENT(ReluN, GetReluNGradient);
} // namespace caffe2
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