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#include "caffe2/operators/clip_op.h"
#include "caffe2/utils/eigen_utils.h"
namespace caffe2 {
template <>
bool ClipOp<float, CPUContext>::RunOnDevice() {
auto& X = Input(0);
auto* Y = Output(0, X.sizes(), at::dtype<float>());
EigenVectorMap<float>(Y->template mutable_data<float>(), Y->numel()) =
ConstEigenVectorMap<float>(X.data<float>(), X.numel())
.cwiseMax(min_)
.cwiseMin(max_);
return true;
}
template <>
bool ClipGradientOp<float, CPUContext>::RunOnDevice() {
auto& Y = Input(0);
auto& dY = Input(1);
CAFFE_ENFORCE_GE(Y.numel(), 0);
CAFFE_ENFORCE_EQ(dY.numel(), Y.numel());
auto* dX = Output(0, Y.sizes(), at::dtype<float>());
const float* Ydata = Y.data<float>();
const float* dYdata = dY.data<float>();
float* dXdata = dX->template mutable_data<float>();
for (int i = 0; i < Y.numel(); ++i) {
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
dXdata[i] = dYdata[i] * (Ydata[i] > min_ && Ydata[i] < max_);
}
return true;
}
REGISTER_CPU_OPERATOR(Clip, ClipOp<float, CPUContext>);
REGISTER_CPU_GRADIENT_OPERATOR(ClipGradient, ClipGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(Clip)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
This operator limits the given input within an interval. The interval is
specified by the `min` and `max` arguments. They default to
*numeric_limits::lowest()* and *numeric_limits::max()* respectively. The
clipping operation can be done in an in-place fashion by using the same output
blob as the input blob.
Github Links:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/clip_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"Clip",
["X"],
["Y"],
min=20.0,
max=60.0
)
workspace.FeedBlob("X", (np.random.randint(100, size=(5,5))).astype(np.float32))
print("X:", workspace.FetchBlob("X"))
workspace.RunOperatorOnce(op)
print("Y:", workspace.FetchBlob("Y"))
```
**Result**
```
X: [[45. 16. 59. 99. 48.]
[12. 44. 46. 82. 28.]
[ 1. 91. 18. 9. 71.]
[24. 37. 61. 12. 81.]
[36. 38. 30. 84. 40.]]
Y: [[45. 20. 59. 60. 48.]
[20. 44. 46. 60. 28.]
[20. 60. 20. 20. 60.]
[24. 37. 60. 20. 60.]
[36. 38. 30. 60. 40.]]
```
</details>
)DOC")
.Arg(
"min",
"*(type: float)* Minimum value, under which element is "
"replaced by min (default=*numeric_limits::lowest()*).")
.Arg(
"max",
"*(type: float)* Maximum value, under which element is "
"replaced by max (default=*numeric_limits::max()*).")
.Input(
0,
"X",
"*(Tensor`<float>`)* Input tensor within range "
"[*numeric_limits::lowest()*, *numeric_limits::max()*].")
.Output(
0,
"Y",
"*(Tensor`<float>`)* Output tensor clipped within range [`min`, `max`].")
.InheritOnnxSchema();
GRADIENT_OPERATOR_SCHEMA(ClipGradient)
.NumInputs(2)
.NumOutputs(1)
.AllowInplace({{1, 0}});
class GetClipGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"ClipGradient", "",
vector<string>{O(0), GO(0)},
vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(Clip, GetClipGradient);
} // namespace caffe2
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