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0dd7769bb1
Rewrite Universal Intrinsic code by using new API: Core module. #23980 The goal of this PR is to match and modify all SIMD code blocks guarded by `CV_SIMD` macro in the `opencv/modules/core` folder and rewrite them by using the new Universal Intrinsic API. The patch is almost auto-generated by using the [rewriter](https://github.com/hanliutong/rewriter), related PR #23885. Most of the files have been rewritten, but I marked this PR as draft because, the `CV_SIMD` macro also exists in the following files, and the reasons why they are not rewrited are: 1. ~~code design for fixed-size SIMD (v_int16x8, v_float32x4, etc.), need to manually rewrite.~~ Rewrited - ./modules/core/src/stat.simd.hpp - ./modules/core/src/matrix_transform.cpp - ./modules/core/src/matmul.simd.hpp 2. Vector types are wrapped in other class/struct, that are not supported by the compiler in variable-length backends. Can not be rewrited directly. - ./modules/core/src/mathfuncs_core.simd.hpp ```cpp struct v_atan_f32 { explicit v_atan_f32(const float& scale) { ... } v_float32 compute(const v_float32& y, const v_float32& x) { ... } ... v_float32 val90; // sizeless type can not used in a class v_float32 val180; v_float32 val360; v_float32 s; }; ``` 3. The API interface does not support/does not match - ./modules/core/src/norm.cpp Use `v_popcount`, ~~waiting for #23966~~ Fixed - ./modules/core/src/has_non_zero.simd.hpp Use illegal Universal Intrinsic API: For float type, there is no logical operation `|`. Further discussion needed ```cpp /** @brief Bitwise OR Only for integer types. */ template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n> operator|(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n>& operator|=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); ``` ```cpp #if CV_SIMD typedef v_float32 v_type; const v_type v_zero = vx_setzero_f32(); constexpr const int unrollCount = 8; int step = v_type::nlanes * unrollCount; int len0 = len & -step; const float* srcSimdEnd = src+len0; int countSIMD = static_cast<int>((srcSimdEnd-src)/step); while(!res && countSIMD--) { v_type v0 = vx_load(src); src += v_type::nlanes; v_type v1 = vx_load(src); src += v_type::nlanes; .... src += v_type::nlanes; v0 |= v1; //Illegal ? .... //res = v_check_any(((v0 | v4) != v_zero));//beware : (NaN != 0) returns "false" since != is mapped to _CMP_NEQ_OQ and not _CMP_NEQ_UQ res = !v_check_all(((v0 | v4) == v_zero)); } v_cleanup(); #endif ``` ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [ ] I agree to contribute to the project under Apache 2 License. - [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [ ] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
122 lines
2.9 KiB
C++
122 lines
2.9 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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#include "opencv2/core/hal/intrin.hpp"
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namespace cv { namespace hal {
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extern const uchar popCountTable[256];
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CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN
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// forward declarations
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int normHamming(const uchar* a, int n);
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int normHamming(const uchar* a, const uchar* b, int n);
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#ifndef CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY
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#if CV_AVX2
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static inline int _mm256_extract_epi32_(__m256i reg, const int i)
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{
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CV_DECL_ALIGNED(32) int reg_data[8];
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CV_DbgAssert(0 <= i && i < 8);
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_mm256_store_si256((__m256i*)reg_data, reg);
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return reg_data[i];
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}
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#endif
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int normHamming(const uchar* a, int n)
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{
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CV_AVX_GUARD;
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int i = 0;
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int result = 0;
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#if (CV_SIMD || CV_SIMD_SCALABLE)
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{
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v_uint64 t = vx_setzero_u64();
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for (; i <= n - VTraits<v_uint8>::vlanes(); i += VTraits<v_uint8>::vlanes())
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t = v_add(t, v_popcount(v_reinterpret_as_u64(vx_load(a + i))));
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result = (int)v_reduce_sum(t);
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vx_cleanup();
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}
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#endif
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#if CV_POPCNT
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{
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# if defined CV_POPCNT_U64
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for(; i <= n - 8; i += 8)
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{
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result += (int)CV_POPCNT_U64(*(uint64*)(a + i));
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}
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# endif
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for(; i <= n - 4; i += 4)
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{
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result += CV_POPCNT_U32(*(uint*)(a + i));
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}
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}
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#endif
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#if CV_ENABLE_UNROLLED
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for(; i <= n - 4; i += 4)
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{
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result += popCountTable[a[i]] + popCountTable[a[i+1]] +
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popCountTable[a[i+2]] + popCountTable[a[i+3]];
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}
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#endif
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for(; i < n; i++)
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{
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result += popCountTable[a[i]];
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}
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return result;
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}
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int normHamming(const uchar* a, const uchar* b, int n)
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{
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CV_AVX_GUARD;
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int i = 0;
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int result = 0;
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#if (CV_SIMD || CV_SIMD_SCALABLE)
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{
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v_uint64 t = vx_setzero_u64();
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for (; i <= n - VTraits<v_uint8>::vlanes(); i += VTraits<v_uint8>::vlanes())
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t = v_add(t, v_popcount(v_reinterpret_as_u64(v_xor(vx_load(a + i), vx_load(b + i)))));
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result += (int)v_reduce_sum(t);
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}
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#endif
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#if CV_POPCNT
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{
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# if defined CV_POPCNT_U64
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for(; i <= n - 8; i += 8)
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{
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result += (int)CV_POPCNT_U64(*(uint64*)(a + i) ^ *(uint64*)(b + i));
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}
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# endif
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for(; i <= n - 4; i += 4)
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{
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result += CV_POPCNT_U32(*(uint*)(a + i) ^ *(uint*)(b + i));
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}
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}
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#endif
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#if CV_ENABLE_UNROLLED
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for(; i <= n - 4; i += 4)
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{
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result += popCountTable[a[i] ^ b[i]] + popCountTable[a[i+1] ^ b[i+1]] +
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popCountTable[a[i+2] ^ b[i+2]] + popCountTable[a[i+3] ^ b[i+3]];
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}
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#endif
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for(; i < n; i++)
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{
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result += popCountTable[a[i] ^ b[i]];
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}
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return result;
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}
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#endif // CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY
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CV_CPU_OPTIMIZATION_NAMESPACE_END
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}} //cv::hal
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