activation.hpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_ACTIVATION_HPP
#define OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_ACTIVATION_HPP
#include "../../op_cuda.hpp"
#include "../csl/stream.hpp"
#include "../csl/tensor.hpp"
#include "../kernels/activations.hpp"
#include <opencv2/core.hpp>
#include <utility>
namespace cv { namespace dnn { namespace cuda4dnn {
template <class T>
class ReLUOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
ReLUOp(csl::Stream stream_, T slope_)
: stream(std::move(stream_)), slope{ slope_ } { }
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
for (int i = 0; i < inputs.size(); i++)
{
auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[i].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
kernels::relu<T>(stream, output, input, slope);
}
}
private:
csl::Stream stream;
const T slope;
};
template <class T>
class ClippedReLUOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
ClippedReLUOp(csl::Stream stream_, T min_, T max_)
: stream(std::move(stream_)), min{ min_ }, max{ max_ } { }
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
for (int i = 0; i < inputs.size(); i++)
{
auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[i].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
kernels::clipped_relu<T>(stream, output, input, min, max);
}
}
private:
csl::Stream stream;
const T min, max;
};
template <class T>
class ChannelwiseReLUOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
ChannelwiseReLUOp(csl::Stream stream_, const Mat& slope)
: stream(std::move(stream_))
{
CV_Assert(!slope.empty());
slopeTensor = csl::makeTensorHeader<T>(slope);
csl::copyMatToTensor<T>(slope, slopeTensor, stream);
}
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
for (int i = 0; i < inputs.size(); i++)
{
auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[i].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
CV_Assert(input.get_axis_size(1) == slopeTensor.size());
std::size_t inner_size = input.size_range(2, input.rank());
kernels::axiswise_relu<T>(stream, output, input, inner_size, slopeTensor);
}
}
private:
csl::Stream stream;
csl::Tensor<T> slopeTensor;
};
template <class T>
class TanHOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
TanHOp(csl::Stream stream_) : stream(std::move(stream_)) { }
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
for (int i = 0; i < inputs.size(); i++)
{
auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[i].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
kernels::tanh<T>(stream, output, input);
}
}
private:
csl::Stream stream;
};
template <class T>
class SwishOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
SwishOp(csl::Stream stream_) : stream(std::move(stream_)) { }
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
for (int i = 0; i < inputs.size(); i++)
{
auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[i].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
kernels::swish<T>(stream, output, input);
}
}
private:
csl::Stream stream;
};
template <class T>
class MishOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
MishOp(csl::Stream stream_) : stream(std::move(stream_)) { }
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
for (int i = 0; i < inputs.size(); i++)
{
auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[i].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
kernels::mish<T>(stream, output, input);
}
}
private:
csl::Stream stream;
};
template <class T>
class SigmoidOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
SigmoidOp(csl::Stream stream_) : stream(std::move(stream_)) { }
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
for (int i = 0; i < inputs.size(); i++)
{
auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[i].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
kernels::sigmoid<T>(stream, output, input);
}
}
private:
csl::Stream stream;
};
template <class T>
class ELUOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
ELUOp(csl::Stream stream_) : stream(std::move(stream_)) { }
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
for (int i = 0; i < inputs.size(); i++)
{
auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[i].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
kernels::elu<T>(stream, output, input);
}
}
private:
csl::Stream stream;
};
template <class T>
class AbsValOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
AbsValOp(csl::Stream stream_) : stream(std::move(stream_)) { }
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
for (int i = 0; i < inputs.size(); i++)
{
auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[i].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
kernels::abs<T>(stream, output, input);
}
}
private:
csl::Stream stream;
};
template <class T>
class BNLLOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
BNLLOp(csl::Stream stream_) : stream(std::move(stream_)) { }
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
for (int i = 0; i < inputs.size(); i++)
{
auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[i].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
kernels::bnll<T>(stream, output, input);
}
}
private:
csl::Stream stream;
};
template <class T>
class PowerOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
PowerOp(csl::Stream stream_, T exp_, T scale_, T shift_)
: stream(std::move(stream_)), exp{ exp_ }, scale{ scale_ }, shift{ shift_ } { }
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
for (int i = 0; i < inputs.size(); i++)
{
auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[i].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
kernels::power<T>(stream, output, input, exp, scale, shift);
}
}
private:
csl::Stream stream;
const T exp, scale, shift;
};
template <class T>
class ExpOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
ExpOp(csl::Stream stream_, T nScale_, T nShift_)
: stream(std::move(stream_)), normScale{ nScale_ }, normShift{ nShift_ } { }
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
for (int i = 0; i < inputs.size(); i++)
{
auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[i].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
kernels::exp<T>(stream, output, input, normScale, normShift);
}
}
private:
csl::Stream stream;
const T normScale, normShift;
};
}}} /* namespace cv::dnn::cuda4dnn */
#endif /* OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_ACTIVATION_HPP */