caffe2/operators/floor_op.cc

#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