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#include "caffe2/operators/thresholded_relu_op.h"
#include "caffe2/utils/eigen_utils.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
template <>
bool ThresholdedReluOp<float, CPUContext>::RunOnDevice() {
auto& X = Input(0);
auto* Y = Output(0, X.sizes(), at::dtype<float>());
ConstEigenVectorArrayMap<float> Xvec(X.data<float>(), X.numel());
EigenVectorArrayMap<float> Yvec(
Y->template mutable_data<float>(), Y->numel());
Yvec = (Xvec > alpha_).select(Xvec, 0.f);
/* Naive implementation
const float* Xdata = X.data<float>();
float* Ydata = Y->template mutable_data<float>();
for (int i = 0; i < X.size(); ++i) {
Xdata[i] -= alpha_;
Ydata[i] = std::max(Xdata[i], 0.0f);
}
*/
return true;
}
template <>
bool ThresholdedReluGradientOp<float, CPUContext>::RunOnDevice() {
auto& Y = Input(0);
auto& dY = Input(1);
CAFFE_ENFORCE_EQ(dY.numel(), Y.numel());
auto* dX = Output(0, Y.sizes(), at::dtype<float>());
const float* Ydata = Y.data<float>();
const float* dYdata = dY.data<float>();
float* dXdata = dX->template mutable_data<float>();
EigenVectorArrayMap<float> dXvec(dXdata, dX->numel());
ConstEigenVectorArrayMap<float> Yvec(Ydata, Y.numel());
ConstEigenVectorArrayMap<float> dYvec(dYdata, dY.numel());
dXvec = dYvec * Yvec.cwiseSign();
/* Non vectorized implementation
for (int i = 0; i < Y.size(); ++i) {
dXdata[i] = Ydata[i] > 0 ? dYdata[i] : 0;
}
*/
return true;
}
REGISTER_CPU_OPERATOR(ThresholdedRelu, ThresholdedReluOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(
ThresholdedReluGradient,
ThresholdedReluGradientOp<float, CPUContext>);
// Input: X, output: Y
OPERATOR_SCHEMA(ThresholdedRelu)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.CostInferenceFunction(PointwiseCostInference<2>)
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
ThresholdedRelu takes one input data (Tensor) and produces one output data
(Tensor) where the rectified linear function, y = x for x > alpha, y = 0
otherwise, is applied to the tensor elementwise.
)DOC")
.Arg("alpha", "(float) defaults to 1.0.")
.Input(0, "X", "1D input tensor")
.Output(0, "Y", "1D input tensor");
// Input: Y, dY, output: dX
OPERATOR_SCHEMA(ThresholdedReluGradient)
.NumInputs(2)
.NumOutputs(1)
.AllowInplace({{1, 0}})
.SetDoc(R"DOC(
ThresholdedReluGradient takes both Y and dY and uses this to update dX
according to the chain rule and derivatives of the rectified linear function.
)DOC");
class GetThresholdedReluGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
def_.type() + "Gradient",
"",
vector<string>{O(0), GO(0)},
vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(ThresholdedRelu, GetThresholdedReluGradient);
} // namespace caffe2
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