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#ifndef CAFFE2_OPERATORS_CLIP_TENSOR_OP_H_
#define CAFFE2_OPERATORS_CLIP_TENSOR_OP_H_
#include <vector>
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
template <typename Context>
class ClipTensorByScalingOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
ClipTensorByScalingOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws) {
threshold_ = this->template GetSingleArgument<float>("threshold", 0.0);
CAFFE_ENFORCE_GT(threshold_, 0, "Threshold must be greater than 0");
}
bool RunOnDevice() override {
const auto& input_tensor = Input(0);
CAFFE_ENFORCE_GT(input_tensor.numel(), 0);
const auto& val = Input(1);
CAFFE_ENFORCE_EQ(val.numel(), 1);
const auto* input_tensor_data = input_tensor.template data<float>();
const auto* val_data = val.template data<float>();
auto* clipped = Output(0, input_tensor.sizes(), at::dtype<float>());
float* clipped_tensor_data = clipped->template mutable_data<float>();
if (InputSize() > 2) {
const auto& additional_threshold = Input(2);
CAFFE_ENFORCE_EQ(additional_threshold.numel(), 1);
threshold_ *= *(additional_threshold.template data<float>());
}
if (*val_data > threshold_) {
float ratio = threshold_ / *val_data;
math::Scale<float, float, Context>(
clipped->numel(),
ratio,
input_tensor_data,
clipped_tensor_data,
&context_);
} else {
if (input_tensor_data != clipped_tensor_data) {
clipped->CopyFrom(input_tensor, /*async*/ true);
}
}
return true;
}
private:
float threshold_;
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_CLIP_TENSOR_OP_H_
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