pytorch/caffe2/operators/scale_blobs_op.h

60 lines
1.5 KiB
C++

#ifndef CAFFE2_OPERATORS_SCALE_BLOBS_OP_H_
#define CAFFE2_OPERATORS_SCALE_BLOBS_OP_H_
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/utils/math.h"
#include "c10/util/irange.h"
namespace caffe2 {
template <class Context>
class ScaleBlobsOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit ScaleBlobsOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
OP_SINGLE_ARG(float, "scale", scale_, 1.0f) {}
template <typename T>
bool DoRunWithType() {
int batchSize = InputSize();
for (const auto i : c10::irange(batchSize)) {
const auto& X = Input(i);
auto* Y = Output(i, X.sizes(), at::dtype<T>());
math::Scale<float, T, Context>(
X.numel(),
scale_,
X.template data<T>(),
Y->template mutable_data<T>(),
&context_);
}
return true;
}
bool RunOnDevice() override {
for (const auto i : c10::irange(InputSize())) {
auto& input = this->template Input<Tensor>(i, CPU);
auto* output = this->template Output<Tensor>(i, CPU);
output->ResizeLike(input);
}
return DispatchHelper<TensorTypes<float>>::call(this, Input(0));
}
private:
const float scale_;
Tensor blobSizes_;
Tensor inputs_;
Tensor outputs_;
Tensor hostBlobSizes_;
Tensor hostInputs_;
Tensor hostOutputs_;
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_SCALE_BLOBS_OP_H_