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#include "caffe2/operators/ceil_op.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(Ceil, CeilOp<float, CPUContext>);
OPERATOR_SCHEMA(Ceil)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.SetDoc(R"DOC(
Element-wise application of the ceil function ($y=ceil(x)$) to the input tensor
`X`. Output tensor shape is the same as the input tensor.
Github Link:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/ceil_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"Ceil",
["X"],
["X"],
)
workspace.FeedBlob("X", (np.random.uniform(-10, 10, (5,5))).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:
[[ 8.44598 -6.5098248 -2.2993476 -7.6859694 0.58566964]
[-7.846551 -0.03689406 6.9362907 -4.0521703 4.4969673 ]
[ 0.33355865 -7.895527 -8.393201 9.374202 -2.3930092 ]
[-6.3061996 3.1403487 3.782099 -8.516556 -2.8387244 ]
[-2.0164998 4.7663913 -3.422966 0.3636999 8.75713 ]]
X after running op:
[[ 9. -6. -2. -7. 1.]
[-7. -0. 7. -4. 5.]
[ 1. -7. -8. 10. -2.]
[-6. 4. 4. -8. -2.]
[-2. 5. -3. 1. 9.]]
```
</details>
)DOC")
.Input(0, "X", "*(type: Tensor`<float>`)* Input tensor.")
.Output(0, "Y", "*(type: Tensor`<float>`)* Output tensor.");
// TODO: Write gradient for this when needed
GRADIENT_NOT_IMPLEMENTED_YET(Ceil);
} // namespace caffe2
|
