| #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 |