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#include "caffe2/operators/exp_op.h"
#include <string>
#include <vector>
namespace caffe2 {
REGISTER_CPU_OPERATOR(
Exp,
UnaryElementwiseOp<TensorTypes<float>, CPUContext, ExpFunctor<CPUContext>>);
OPERATOR_SCHEMA(Exp)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
Calculates the exponential of the given input tensor ($exp(x)$), element-wise. This
operation can be done in an in-place fashion too, by providing the same input
and output blobs.
Github Link:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/exp_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"Exp",
["X"],
["X"],
)
workspace.FeedBlob("X", (np.random.rand(3,3)).astype(np.float32))
print("X before running op:", workspace.FetchBlob("X"))
workspace.RunOperatorOnce(op)
print("X after running op:", workspace.FetchBlob("X"))
```
**Result**
```
X before running op:
[[0.5821691 0.07719802 0.50159824]
[0.40952456 0.36788362 0.84887683]
[0.02472685 0.65730894 0.9066397 ]]
X after running op:
[[1.7899168 1.080256 1.6513585]
[1.5061016 1.4446739 2.3370204]
[1.0250351 1.9295927 2.4759884]]
```
</details>
)DOC")
.Input(0, "X", "*(type: Tensor`<float>`)* Input tensor.")
.Output(
0,
"Y",
"*(type: Tensor`<float>`)* The exponential of the input tensor computed "
"element-wise.")
.InheritOnnxSchema();
namespace {
class GetExpGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
std::vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"Mul",
"",
std::vector<std::string>{O(0), GO(0)},
std::vector<std::string>{GI(0)});
}
};
} // namespace
REGISTER_GRADIENT(Exp, GetExpGradient);
} // namespace caffe2
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