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#include "caffe2/operators/sqr_op.h"
#include <string>
#include <vector>
namespace caffe2 {
REGISTER_CPU_OPERATOR(
Sqr,
UnaryElementwiseOp<TensorTypes<float>, CPUContext, SqrFunctor<CPUContext>>);
OPERATOR_SCHEMA(Sqr)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
Performs element-wise squaring ($x^2$) of input tensor.
Github Link:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/sqr_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"Sqr",
["X"],
["Y"],
)
workspace.FeedBlob("X", (np.random.randint(10, size=(3,3))).astype(np.float32))
print("X:", workspace.FetchBlob("X"))
workspace.RunOperatorOnce(op)
print("Y:", workspace.FetchBlob("Y"))
```
**Result**
```
X:
[[4. 6. 2.]
[0. 1. 6.]
[9. 2. 7.]]
Y:
[[16. 36. 4.]
[ 0. 1. 36.]
[81. 4. 49.]]
```
</details>
)DOC")
.Input(0, "X", "*(type: Tensor`<float>`)* Input data tensor.")
.Output(0, "Y", "*(type: Tensor`<float>`)* Output tensor.");
namespace {
class GetSqrGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
std::vector<OperatorDef> GetGradientDefs() override {
Argument scale_arg;
scale_arg.set_name("scale");
scale_arg.set_f(2.0);
return std::vector<OperatorDef>{CreateOperatorDef(
"Scale",
"",
std::vector<std::string>{GO(0)},
std::vector<std::string>{GO(0)},
std::vector<Argument>{scale_arg}),
CreateOperatorDef(
"Mul",
"",
std::vector<std::string>{GO(0), I(0)},
std::vector<std::string>{GI(0)})};
}
};
} // namespace
REGISTER_GRADIENT(Sqr, GetSqrGradient);
} // namespace caffe2
|
