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#include "caffe2/operators/negative_op.h"
#include <string>
#include <vector>
namespace caffe2 {
REGISTER_CPU_OPERATOR(
Negative,
UnaryElementwiseOp<NumericTypes, CPUContext, NegativeFunctor<CPUContext>>);
// Input: X, output: Y
OPERATOR_SCHEMA(Negative)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
Computes the element-wise negative of the input.
Github Links:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/negative_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"Negative",
["X"],
["Y"]
)
workspace.FeedBlob("X", (np.random.rand(3,3).astype(np.float32)))
print("X:", workspace.FetchBlob("X"))
workspace.RunOperatorOnce(op)
print("Y:", workspace.FetchBlob("Y"))
```
**Result**
```
X: [[0.83296907 0.61407167 0.32562155]
[0.59304523 0.03111175 0.29365504]
[0.09478621 0.5424558 0.73940724]]
Y: [[-0.83296907 -0.61407167 -0.32562155]
[-0.59304523 -0.03111175 -0.29365504]
[-0.09478621 -0.5424558 -0.73940724]]
```
</details>
)DOC")
.Input(0, "X", "*(type: Tensor`<float>`)* 1D input tensor.")
.Output(0, "Y", "*(type: Tensor`<float>`)* 1D output tensor.")
.InheritOnnxSchema("Neg");
namespace {
class GetNegativeGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
std::vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"Negative",
"",
std::vector<std::string>{GO(0)},
std::vector<std::string>{GI(0)});
}
};
} // namespace
REGISTER_GRADIENT(Negative, GetNegativeGradient);
} // namespace caffe2
|
