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#include "caffe2/operators/hard_sigmoid_op.h"
#include <algorithm>
#include <functional>
#include <string>
#include "caffe2/utils/eigen_utils.h"
namespace caffe2 {
template <>
template <typename T>
bool HardSigmoidFunctor<CPUContext>::
operator()(const int N, const T* X, T* Y, CPUContext* /* context */) const {
EigenVectorArrayMap<T>(Y, N) =
(ConstEigenVectorArrayMap<T>(X, N) * T(alpha) + T(beta))
.cwiseMin(T(1))
.cwiseMax(T(0));
return true;
}
template <>
template <typename T>
bool HardSigmoidGradientFunctor<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(1))
.select(ConstEigenVectorArrayMap<T>(dY, size) * alpha, T(0));
return true;
}
namespace {
OpSchema::Cost CostInferenceForHardSigmoid(
const OperatorDef& def,
const vector<TensorShape>& in) {
struct OpSchema::Cost cost = PointwiseCostInference<4>(def, in);
cost.params_bytes = 0;
return cost;
}
} // namespace
REGISTER_CPU_OPERATOR(
HardSigmoid,
UnaryElementwiseWithArgsOp<
TensorTypes<float>,
CPUContext,
HardSigmoidFunctor<CPUContext>>);
REGISTER_CPU_OPERATOR(
HardSigmoidGradient,
BinaryElementwiseWithArgsOp<
TensorTypes<float>,
CPUContext,
HardSigmoidGradientFunctor<CPUContext>>);
// Input: X, output: Y
OPERATOR_SCHEMA(HardSigmoid)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.CostInferenceFunction(CostInferenceForHardSigmoid)
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
Applies hard sigmoid operation to the input data element-wise.
The HardSigmoid operation takes one input $X$, produces one output $Y$, and is defined as:
$$Y = max(0,min(1,x * alpha + beta))$$
Github Links:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/hard_sigmoid_op.h
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/hard_sigmoid_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"HardSigmoid",
["X"],
["Y"],
alpha = 0.2,
beta = 0.5,
)
workspace.FeedBlob("X", np.random.randn(5).astype(np.float32))
print("input:", workspace.FetchBlob("X"))
workspace.RunOperatorOnce(op)
print("sigmoid:", workspace.FetchBlob("Y"))
```
**Result**
```
input: [ 1.5744036 0.31632107 1.7842269 1.4450722 -2.1726978 ]
hard_sigmoid: [ 0.81488073, 0.56326419, 0.85684538, 0.78901446, 0.06546044]
```
</details>
)DOC")
.Arg("alpha", "float: the slope of the function. Defaults to 0.2")
.Arg("beta", "float: the bias value of the function. Defaults to 0.5")
.Input(0, "X", "1D input tensor")
.Output(0, "Y", "1D output tensor with same shape as input")
.InheritOnnxSchema();
// Input: Y, dY, output: dX
OPERATOR_SCHEMA(HardSigmoidGradient)
.NumInputs(2)
.NumOutputs(1)
.AllowInplace({{1, 0}})
.SetDoc(R"DOC(
HardSigmoidGradient takes both Y and dY as well as an argument alpha and uses
this to update dX according to the chain rule and derivatives of the hard
sigmoid function.
)DOC");
namespace {
class GetHardSigmoidGradient : 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(HardSigmoid, GetHardSigmoidGradient);
} // namespace caffe2
|
