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119 lines
4.4 KiB
C++
119 lines
4.4 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#ifndef OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_ELTWISE_HPP
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#define OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_ELTWISE_HPP
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#include "../../op_cuda.hpp"
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#include "../csl/stream.hpp"
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#include "../csl/tensor.hpp"
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#include "../csl/tensor_ops.hpp"
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#include "../kernels/eltwise_ops.hpp"
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#include <opencv2/core.hpp>
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#include <cstddef>
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#include <vector>
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#include <utility>
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namespace cv { namespace dnn { namespace cuda4dnn {
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enum class EltwiseOpType {
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MAX,
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SUM,
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PRODUCT,
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DIV
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};
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template <class T>
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class EltwiseOp final : public CUDABackendNode {
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public:
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using wrapper_type = GetCUDABackendWrapperType<T>;
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template <class V>
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EltwiseOp(csl::Stream stream_, EltwiseOpType op_, std::vector<V> coeffs_)
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: stream(std::move(stream_)), op{ op_ }, coeffs(std::begin(coeffs_), std::end(coeffs_))
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{
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}
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void forward(
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const std::vector<cv::Ptr<BackendWrapper>>& inputs,
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const std::vector<cv::Ptr<BackendWrapper>>& outputs,
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csl::Workspace& workspace) override
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{
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CV_Assert(inputs.size() >= 2);
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CV_Assert(outputs.size() == 1);
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CV_Assert(coeffs.size() == 0 || op == EltwiseOpType::SUM);
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CV_Assert(coeffs.size() == 0 || inputs.size() == coeffs.size());
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auto output_wrapper = outputs[0].dynamicCast<wrapper_type>();
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auto output = output_wrapper->getSpan();
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if (inputs.size() == 2)
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{
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auto input_wrapper_x = inputs[0].dynamicCast<wrapper_type>();
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auto input_x = input_wrapper_x->getView();
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auto input_wrapper_y = inputs[1].dynamicCast<wrapper_type>();
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auto input_y = input_wrapper_y->getView();
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switch (op)
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{
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case EltwiseOpType::MAX: kernels::eltwise_max_2<T>(stream, output, input_x, input_y); break;
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case EltwiseOpType::PRODUCT: kernels::eltwise_prod_2<T>(stream, output, input_x, input_y); break;
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case EltwiseOpType::DIV: kernels::eltwise_div_2<T>(stream, output, input_x, input_y); break;
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case EltwiseOpType::SUM:
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if (coeffs.empty() || (coeffs[0] == 1 && coeffs[1] == 1))
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kernels::eltwise_sum_2<T>(stream, output, input_x, input_y);
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else
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kernels::eltwise_sum_coeff_2<T>(stream, output, coeffs[0], input_x, coeffs[1], input_y);
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break;
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}
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}
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else
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{
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auto input_wrapper_0 = inputs[0].dynamicCast<wrapper_type>();
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auto input_0 = input_wrapper_0->getView();
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/* we first make a copy and then apply EltwiseOp cumulatively */
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csl::tensor_ops::copy(stream, output, input_0);
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for (int i = 1; i < inputs.size(); i++)
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{
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auto input_wrapper = inputs[i].dynamicCast<wrapper_type>();
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auto input = input_wrapper->getView();
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switch (op)
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{
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case EltwiseOpType::MAX: kernels::eltwise_max_2<T>(stream, output, output, input); break;
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case EltwiseOpType::PRODUCT: kernels::eltwise_prod_2<T>(stream, output, output, input); break;
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case EltwiseOpType::DIV: kernels::eltwise_div_2<T>(stream, output, output, input); break;
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case EltwiseOpType::SUM:
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if (coeffs.empty() || coeffs[i] == 1)
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kernels::eltwise_sum_2<T>(stream, output, output, input);
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else
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{
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/* if this is the first op, we must scale output too */
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auto coeff_x = (i == 1) ? coeffs[0] : static_cast<T>(1.0);
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kernels::eltwise_sum_coeff_2<T>(stream, output, coeff_x, output, coeffs[i], input);
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}
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break;
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}
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}
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}
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}
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private:
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csl::Stream stream;
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EltwiseOpType op;
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std::vector<T> coeffs;
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};
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}}} /* namespace cv::dnn::cuda4dnn */
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#endif /* OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_ELTWISE_HPP */
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