blob: 72fe0eb59a5346cb06ff6d226de1a45fe7725fa4 [file] [log] [blame]
#include "caffe2/operators/math_ops.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
struct SqrCPUFunctor {
template <typename T>
inline void
operator()(const int n, const T* x, T* y, CPUContext* device_context) {
math::Sqr<T, CPUContext>(n, x, y, device_context);
}
};
REGISTER_CPU_OPERATOR(
Sqr,
UnaryElementwiseOp<TensorTypes<float>, CPUContext, SqrCPUFunctor>);
OPERATOR_SCHEMA(Sqr)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.IdenticalTypeAndShape()
.SetDoc("Square (x^2) the elements of the input")
.Input(0, "input", "Input tensor")
.Output(0, "output", "Squared elements of the input");
class GetSqrGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
Argument scale_arg;
scale_arg.set_name("scale");
scale_arg.set_f(2.0);
return vector<OperatorDef>{CreateOperatorDef(
"Scale",
"",
std::vector<string>{GO(0)},
std::vector<string>{GO(0)},
std::vector<Argument>{scale_arg}),
CreateOperatorDef(
"Mul",
"",
std::vector<string>{GO(0), I(0)},
std::vector<string>{GI(0)})};
}
};
REGISTER_GRADIENT(Sqr, GetSqrGradient);
struct SignCPUFunctor {
template <typename T>
inline void
operator()(const int n, const T* x, T* y, CPUContext* device_context) {
for (int i = 0; i < n; ++i) {
y[i] = (-T(1) * (x[i] < 0)) + (x[i] > 0);
}
}
};
REGISTER_CPU_OPERATOR(
Sign,
UnaryElementwiseOp<TensorTypes<float>, CPUContext, SignCPUFunctor>);
OPERATOR_SCHEMA(Sign)
.NumInputs(1)
.NumOutputs(1)
.SetDoc("Computes sign for each element of the input: -1, 0 or 1.")
.IdenticalTypeAndShape();
SHOULD_NOT_DO_GRADIENT(Sign);
} // namespace caffe2