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Summary: As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH` All changes but the ones to `.clang-tidy` are generated using following script: ``` for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008 Reviewed By: driazati, r-barnes Differential Revision: D29838584 Pulled By: malfet fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
92 lines
2.0 KiB
C++
92 lines
2.0 KiB
C++
#include "caffe2/operators/sigmoid_op.h"
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#include "caffe2/utils/eigen_utils.h"
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namespace caffe2 {
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template <>
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template <typename T>
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bool SigmoidFunctor<CPUContext>::
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operator()(const int N, const T* X, T* Y, CPUContext* /* context */) const {
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EigenVectorArrayMap<T>(Y, N) =
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T(1) / (T(1) + (-ConstEigenVectorArrayMap<T>(X, N)).exp());
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return true;
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}
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REGISTER_CPU_OPERATOR(
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Sigmoid,
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UnaryElementwiseOp<
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TensorTypes<float>,
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CPUContext,
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SigmoidFunctor<CPUContext>>);
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// Input: X, output: Y
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OPERATOR_SCHEMA(Sigmoid)
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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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Apply the Sigmoid function element-wise to the input tensor. This is often used
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as a non-linear activation function in a neural network. The sigmoid function is
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defined as:
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$$Sigmoid(x) = \frac{1}{1+\exp(-x)}$$
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Github Links:
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- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/sigmoid_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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"Sigmoid",
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["X"],
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["Y"]
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)
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workspace.FeedBlob("X", np.random.randn(5).astype(np.float32))
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print("input:", workspace.FetchBlob("X"))
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workspace.RunOperatorOnce(op)
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print("sigmoid:", workspace.FetchBlob("Y"))
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```
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**Result**
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```
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input: [ 1.5744036 0.31632107 1.7842269 1.4450722 -2.1726978 ]
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sigmoid: [0.8284105 0.57842743 0.85621804 0.80923885 0.10222916]
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```
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</details>
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)DOC")
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.Input(0, "X", "*(type: Tensor`<float>`)* Input tensor.")
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.Output(0, "Y", "*(type: Tensor`<float>`)* Output tensor.")
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.InheritOnnxSchema();
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// Input: Y, dY, output: dX
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OPERATOR_SCHEMA(SigmoidGradient)
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.NumInputs(2)
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.NumOutputs(1)
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.AllowInplace({{1, 0}})
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.IdenticalTypeAndShapeOfInput(1)
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.SetDoc(R"DOC(
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SigmoidGradient takes both Y and dY and uses this to update dX according to the
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chain rule and derivatives of the sigmoid function.
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)DOC");
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} // namespace caffe2
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