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#include "caffe2/operators/floor_op.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(Floor, FloorOp<float, CPUContext>);
OPERATOR_SCHEMA(Floor)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.SetDoc(R"DOC(
Element-wise application of the floor function ($y=floor(x)$) to the input
tensor `X`. Output tensor shape is the same as the input tensor. This
operator can be used in an in-place fashion by using the same input blob as the
output blob.
Github Link:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/floor_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"Floor",
["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:
[[ 3.813361 -1.319647 5.2089314 -4.931328 0.6218652 ]
[ 7.2757645 5.5552588 5.785643 -2.4790506 -0.41400087]
[ 1.1541046 -6.933266 3.3754056 1.6569928 -1.7670316 ]
[-3.4932013 4.891472 1.5530115 -3.2443287 -4.605099 ]
[-4.574543 -7.360948 5.91305 -8.196495 -5.357458 ]]
X after running op:
[[ 3. -2. 5. -5. 0.]
[ 7. 5. 5. -3. -1.]
[ 1. -7. 3. 1. -2.]
[-4. 4. 1. -4. -5.]
[-5. -8. 5. -9. -6.]]
```
</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(Floor);
} // namespace caffe2
|
