151 lines
3.6 KiB
C++
151 lines
3.6 KiB
C++
#include "caffe2/operators/softsign_op.h"
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#include "caffe2/utils/eigen_utils.h"
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#include <algorithm>
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#include <functional>
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namespace caffe2 {
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template <>
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template <typename T>
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bool SoftsignFunctor<CPUContext>::
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operator()(const int N, const T* X, T* Y, CPUContext* /* context */) const {
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ConstEigenVectorArrayMap<T> X_arr(X, N);
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EigenVectorMap<T>(Y, N) = (T(1) + X_arr.abs()).inverse() * X_arr;
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return true;
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}
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template <>
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template <typename T>
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bool SoftsignGradientFunctor<CPUContext>::Forward(
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const std::vector<int>& X_dims,
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const std::vector<int>& /* dY_dims */,
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const T* X,
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const T* dY,
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T* dX,
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CPUContext* /* context */) const {
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const int size = std::accumulate(
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// NOLINTNEXTLINE(modernize-use-transparent-functors)
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X_dims.cbegin(), X_dims.cend(), 1, std::multiplies<int>());
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ConstEigenVectorArrayMap<T> dY_arr(dY, size);
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ConstEigenVectorArrayMap<T> X_arr(X, size);
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EigenVectorMap<T>(dX, size) =
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dY_arr * (T(1) + X_arr.abs()).square().inverse();
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return true;
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}
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REGISTER_CPU_OPERATOR(
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Softsign,
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UnaryElementwiseOp<
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TensorTypes<float>,
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CPUContext,
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SoftsignFunctor<CPUContext>>);
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REGISTER_CPU_GRADIENT_OPERATOR(
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SoftsignGradient,
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BinaryElementwiseOp<
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TensorTypes<float>,
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CPUContext,
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SoftsignGradientFunctor<CPUContext>>);
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OPERATOR_SCHEMA(Softsign)
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.NumInputs(1)
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.NumOutputs(1)
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.AllowInplace({{0, 0}})
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.IdenticalTypeAndShape()
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.SetDoc(R"DOC(
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*Softsign* takes one input data tensor $X$ and produces one output data $Y,$ where the softsign function, $y = \frac{x}{1+ |x|}$, is applied to $X$ elementwise. This operation can be done in an in-place fashion too, by providing the same input and output blobs.
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Github Links:
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- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/softsign_op.cc
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<details>
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<summary> <b>Example</b> </summary>
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**Code**
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```
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workspace.ResetWorkspace()
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op = core.CreateOperator(
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"Softsign",
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["X"],
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["Y"],
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)
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workspace.FeedBlob("X", np.random.randn(3, 3).astype(np.float32))
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print("X:\n", workspace.FetchBlob("X"), "\n")
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workspace.RunOperatorOnce(op)
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print("Y:\n", workspace.FetchBlob("Y"))
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```
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**Result**
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```
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X:
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[[-1.3060539 0.7242748 -1.9907674 ]
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[-0.64802396 -0.03244735 0.7455406 ]
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[-0.298492 -0.5774271 2.8364444 ]]
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Y:
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[[-0.5663588 0.420046 -0.6656376 ]
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[-0.39321268 -0.03142761 0.4271116 ]
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[-0.2298759 -0.36605626 0.739342 ]]
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```
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</details>
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)DOC")
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.Input(0, "input", "Input data blob to be operated on.")
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.Output(0, "output", "Output data blob with same shape as input")
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.InheritOnnxSchema();
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GRADIENT_OPERATOR_SCHEMA(SoftsignGradient)
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.NumInputs(2)
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.NumOutputs(1)
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.AllowInplace({{1, 0}})
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.SetDoc(R"DOC(
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Calculates the softsign gradient (sgn(x)/(1+|x|)^2) of the given input tensor
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element-wise.
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)DOC")
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.Input(0, "input", "1-D input tensor")
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.Input(1, "input", "1-D input tensor")
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.Output(
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0,
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"output",
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"The softsign gradient (sgn(x)/(1+|x|)^2) values of the input tensor "
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"computed element-wise");
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namespace {
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class GetSoftsignGradient : public GradientMakerBase {
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using GradientMakerBase::GradientMakerBase;
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std::vector<OperatorDef> GetGradientDefs() override {
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CAFFE_ENFORCE(
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I(0) != O(0),
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"Cannot compute softsign gradient "
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"if you choose to do an in-place calculation.");
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return SingleGradientDef(
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"SoftsignGradient",
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"",
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std::vector<std::string>{I(0), GO(0)},
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std::vector<std::string>{GI(0)});
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}
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};
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} // namespace
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REGISTER_GRADIENT(Softsign, GetSoftsignGradient);
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} // namespace caffe2
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