caffe2/operators/sigmoid_op.cc

#include "caffe2/operators/sigmoid_op.h"

#include "caffe2/utils/eigen_utils.h"

namespace caffe2 {

template <>
template <typename T>
bool SigmoidFunctor<CPUContext>::
operator()(const int N, const T* X, T* Y, CPUContext* /* context */) const {
  EigenVectorArrayMap<T>(Y, N) =
      T(1) / (T(1) + (-ConstEigenVectorArrayMap<T>(X, N)).exp());
  return true;
}

REGISTER_CPU_OPERATOR(
    Sigmoid,
    UnaryElementwiseOp<
        TensorTypes<float>,
        CPUContext,
        SigmoidFunctor<CPUContext>>);

// Input: X, output: Y
OPERATOR_SCHEMA(Sigmoid)
    .NumInputs(1)
    .NumOutputs(1)
    .AllowInplace({{0, 0}})
    .IdenticalTypeAndShape()
    .SetDoc(R"DOC(
Apply the Sigmoid function element-wise to the input tensor. This is often used
as a non-linear activation function in a neural network. The sigmoid function is
defined as:

$$Sigmoid(x) = \frac{1}{1+\exp(-x)}$$

Github Links:

- https://github.com/pytorch/pytorch/blob/main/caffe2/operators/sigmoid_op.cc


<details>

<summary> <b>Example</b> </summary>

**Code**

```

workspace.ResetWorkspace()

op = core.CreateOperator(
    "Sigmoid",
    ["X"],
    ["Y"]
)

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 ]
sigmoid: [0.8284105  0.57842743 0.85621804 0.80923885 0.10222916]

```

</details>


)DOC")
    .Input(0, "X", "*(type: Tensor`<float>`)* Input tensor.")
    .Output(0, "Y", "*(type: Tensor`<float>`)* Output tensor.")
    .InheritOnnxSchema();
// Input: Y, dY, output: dX
OPERATOR_SCHEMA(SigmoidGradient)
    .NumInputs(2)
    .NumOutputs(1)
    .AllowInplace({{1, 0}})
    .IdenticalTypeAndShapeOfInput(1)
    .SetDoc(R"DOC(
SigmoidGradient takes both Y and dY and uses this to update dX according to the
chain rule and derivatives of the sigmoid function.
)DOC");

} // namespace caffe2