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d981d04c76
cuda4dnn: optimizations for swish, mish, sigmoid, region, resize based ops, transpose, identity-conv fusion * bunch of optimizations * more accurate implementation for mish
121 lines
3.6 KiB
C++
121 lines
3.6 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_CUDA_VECTOR_TRAITS_HPP
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#define OPENCV_DNN_SRC_CUDA_VECTOR_TRAITS_HPP
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#include <cuda_runtime.h>
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#include "types.hpp"
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#include "memory.hpp"
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#include "../cuda4dnn/csl/pointer.hpp"
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#include <type_traits>
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namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace device {
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/** \file vector_traits.hpp
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* \brief utility classes and functions for vectorized memory loads/stores
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*
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* Example:
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* using vector_type = get_vector_type_t<float, 4>;
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*
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* auto input_vPtr = type::get_pointer(iptr); // iptr is of type DevicePtr<const float>
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* auto output_vPtr = type::get_pointer(optr); // optr is of type DevicePtr<float>
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*
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* vector_type vec;
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* v_load(vec, input_vPtr);
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*
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* for(int i = 0; i < vector_type::size(); i++)
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* vec[i] = do_something(vec[i]);
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*
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* v_store(output_vPtr, vec);
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*/
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namespace detail {
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template <size_type N> struct raw_type_ { };
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template <> struct raw_type_<256> { typedef ulonglong4 type; };
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template <> struct raw_type_<128> { typedef uint4 type; };
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template <> struct raw_type_<64> { typedef uint2 type; };
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template <> struct raw_type_<32> { typedef uint1 type; };
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template <> struct raw_type_<16> { typedef uchar2 type; };
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template <> struct raw_type_<8> { typedef uchar1 type; };
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template <size_type N> struct raw_type {
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using type = typename raw_type_<N>::type;
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static_assert(sizeof(type) * 8 == N, "");
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};
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}
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/* \tparam T type of element in the vector
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* \tparam N "number of elements" of type T in the vector
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*/
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template <class T, size_type N>
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union vector_type {
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using value_type = T;
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using raw_type = typename detail::raw_type<N * sizeof(T) * 8>::type;
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__device__ vector_type() { }
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__device__ static constexpr size_type size() { return N; }
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raw_type raw;
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T data[N];
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template <class U> static __device__
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typename std::enable_if<std::is_const<U>::value, const vector_type*>
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::type get_pointer(csl::DevicePtr<U> ptr) {
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return reinterpret_cast<const vector_type*>(ptr.get());
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}
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template <class U> static __device__
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typename std::enable_if<!std::is_const<U>::value, vector_type*>
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::type get_pointer(csl::DevicePtr<U> ptr) {
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return reinterpret_cast<vector_type*>(ptr.get());
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}
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};
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template <class V>
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__device__ void v_load(V& dest, const V& src) {
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dest.raw = src.raw;
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}
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template <class V>
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__device__ void v_load(V& dest, const V* src) {
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dest.raw = src->raw;
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}
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template <class V>
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__device__ void v_load_ldg(V& dest, const V& src) {
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dest.raw = load_ldg(src.raw);
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}
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template <class V>
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__device__ void v_load_ldg(V& dest, const V* src) {
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dest.raw = load_ldg(src->raw);
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}
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template <class V>
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__device__ void v_store(V* dest, const V& src) {
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dest->raw = src.raw;
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}
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template <class V>
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__device__ void v_store(V& dest, const V& src) {
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dest.raw = src.raw;
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}
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template <class T, size_type N>
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struct get_vector_type {
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typedef vector_type<T, N> type;
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};
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template <class T, size_type N>
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using get_vector_type_t = typename get_vector_type<T, N>::type;
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}}}}} /* namespace cv::dnn::cuda4dnn::csl::device */
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#endif /* OPENCV_DNN_SRC_CUDA_VECTOR_TRAITS_HPP */
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