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