| #include "caffe2/operators/relu_op.h" |
| |
| #include "caffe2/utils/math.h" |
| |
| namespace caffe2 { |
| |
| template <> |
| bool ReluOp<float, CPUContext>::RunOnDevice() { |
| auto& X = Input(0); |
| auto* Y = Output(0); |
| Y->ResizeLike(X); |
| |
| #ifdef CAFFE2_USE_ACCELERATE |
| const float zero = 0.0f; |
| vDSP_vthres(X.data<float>(), 1, &zero, Y->mutable_data<float>(), 1, X.size()); |
| #else |
| EigenVectorMap<float>(Y->mutable_data<float>(), X.size()) = |
| ConstEigenVectorMap<float>(X.data<float>(), X.size()).cwiseMax(0.f); |
| #endif |
| /* Naive implementation |
| const float* Xdata = X.data<float>(); |
| float* Ydata = Y->mutable_data<float>(); |
| for (int i = 0; i < X.size(); ++i) { |
| Ydata[i] = std::max(Xdata[i], 0.f); |
| } |
| */ |
| return true; |
| } |
| |
| template <> |
| bool ReluGradientOp<float, CPUContext>::RunOnDevice() { |
| auto& Y = Input(0); |
| auto& dY = Input(1); |
| auto* dX = Output(0); |
| DCHECK_EQ(dY.size(), Y.size()); |
| dX->ResizeLike(Y); |
| |
| const float* Ydata = Y.data<float>(); |
| const float* dYdata = dY.data<float>(); |
| float* dXdata = dX->mutable_data<float>(); |
| CAFFE2_OMP_PARALLEL_FOR() |
| for (int i = 0; i < Y.size(); ++i) { |
| dXdata[i] = Ydata[i] > 0 ? dYdata[i] : 0; |
| } |
| return true; |
| } |
| |
| namespace { |
| REGISTER_CPU_OPERATOR(Relu, ReluOp<float, CPUContext>); |
| REGISTER_CPU_OPERATOR(ReluGradient, ReluGradientOp<float, CPUContext>); |
| |
| // Input: X, output: Y |
| OPERATOR_SCHEMA(Relu) |
| .NumInputs(1) |
| .NumOutputs(1) |
| .AllowInplace({{0, 0}}) |
| .IdenticalTypeAndShape() |
| .SetDoc(R"DOC( |
| Relu takes one input data (Tensor<T>) and produces one output data |
| (Tensor<T>) where the rectified linear function, y = max(0, x), 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(ReluGradient) |
| .NumInputs(2) |
| .NumOutputs(1) |
| .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"); |
| |
| class GetReluGradient : 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(Relu, GetReluGradient); |
| REGISTER_GRADIENT(ReluFp16, GetReluGradient); |
| |
| } // namespace |
| } // namespace caffe2 |