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https://github.com/opencv/opencv.git
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Merge pull request #8843 from terfendail:resizenn_patch
This commit is contained in:
Vadim Pisarevsky
commit
e72e34ec36
@ -417,6 +417,300 @@ private:
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resizeNNInvoker& operator=(const resizeNNInvoker&);
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};
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#if CV_AVX2
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class resizeNNInvokerAVX4 :
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public ParallelLoopBody
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{
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public:
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resizeNNInvokerAVX4(const Mat& _src, Mat &_dst, int *_x_ofs, int _pix_size4, double _ify) :
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ParallelLoopBody(), src(_src), dst(_dst), x_ofs(_x_ofs), pix_size4(_pix_size4),
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ify(_ify)
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{
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}
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#pragma optimization_parameter target_arch=AVX
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virtual void operator() (const Range& range) const
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{
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Size ssize = src.size(), dsize = dst.size();
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int y, x, pix_size = (int)src.elemSize();
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int width = dsize.width;
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int avxWidth = width - (width & 0x7);
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const __m256i CV_DECL_ALIGNED(64) mask = _mm256_set1_epi32(-1);
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if(((int64)(dst.data + dst.step) & 0x1f) == 0)
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{
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for(y = range.start; y < range.end; y++)
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{
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uchar* D = dst.data + dst.step*y;
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uchar* Dstart = D;
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int sy = std::min(cvFloor(y*ify), ssize.height-1);
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const uchar* S = src.data + sy*src.step;
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#pragma unroll(4)
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for(x = 0; x < avxWidth; x += 8)
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{
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const __m256i CV_DECL_ALIGNED(64) *addr = (__m256i*)(x_ofs + x);
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__m256i CV_DECL_ALIGNED(64) indices = _mm256_lddqu_si256(addr);
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__m256i CV_DECL_ALIGNED(64) pixels = _mm256_i32gather_epi32((const int*)S, indices, 1);
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_mm256_maskstore_epi32((int*)D, mask, pixels);
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D += 32;
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}
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for(; x < width; x++)
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{
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*(int*)(Dstart + x*4) = *(int*)(S + x_ofs[x]);
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}
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}
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}
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else
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{
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for(y = range.start; y < range.end; y++)
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{
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uchar* D = dst.data + dst.step*y;
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uchar* Dstart = D;
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int sy = std::min(cvFloor(y*ify), ssize.height-1);
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const uchar* S = src.data + sy*src.step;
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#pragma unroll(4)
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for(x = 0; x < avxWidth; x += 8)
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{
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const __m256i CV_DECL_ALIGNED(64) *addr = (__m256i*)(x_ofs + x);
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__m256i CV_DECL_ALIGNED(64) indices = _mm256_lddqu_si256(addr);
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__m256i CV_DECL_ALIGNED(64) pixels = _mm256_i32gather_epi32((const int*)S, indices, 1);
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_mm256_storeu_si256((__m256i*)D, pixels);
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D += 32;
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}
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for(; x < width; x++)
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{
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*(int*)(Dstart + x*4) = *(int*)(S + x_ofs[x]);
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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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const Mat src;
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Mat dst;
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int* x_ofs, pix_size4;
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double ify;
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resizeNNInvokerAVX4(const resizeNNInvokerAVX4&);
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resizeNNInvokerAVX4& operator=(const resizeNNInvokerAVX4&);
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};
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class resizeNNInvokerAVX2 :
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public ParallelLoopBody
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{
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public:
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resizeNNInvokerAVX2(const Mat& _src, Mat &_dst, int *_x_ofs, int _pix_size4, double _ify) :
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ParallelLoopBody(), src(_src), dst(_dst), x_ofs(_x_ofs), pix_size4(_pix_size4),
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ify(_ify)
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{
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}
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#pragma optimization_parameter target_arch=AVX
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virtual void operator() (const Range& range) const
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{
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Size ssize = src.size(), dsize = dst.size();
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int y, x, pix_size = (int)src.elemSize();
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int width = dsize.width;
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//int avxWidth = (width - 1) - ((width - 1) & 0x7);
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int avxWidth = width - (width & 0xf);
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const __m256i CV_DECL_ALIGNED(64) mask = _mm256_set1_epi32(-1);
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const __m256i CV_DECL_ALIGNED(64) shuffle_mask = _mm256_set_epi8(15,14,11,10,13,12,9,8,7,6,3,2,5,4,1,0,
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15,14,11,10,13,12,9,8,7,6,3,2,5,4,1,0);
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const __m256i CV_DECL_ALIGNED(64) permute_mask = _mm256_set_epi32(7, 5, 3, 1, 6, 4, 2, 0);
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const __m256i CV_DECL_ALIGNED(64) shift_shuffle_mask = _mm256_set_epi8(13,12,15,14,9,8,11,10,5,4,7,6,1,0,3,2,
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13,12,15,14,9,8,11,10,5,4,7,6,1,0,3,2);
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if(((int64)(dst.data + dst.step) & 0x1f) == 0)
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{
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for(y = range.start; y < range.end; y++)
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{
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uchar* D = dst.data + dst.step*y;
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uchar* Dstart = D;
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int sy = std::min(cvFloor(y*ify), ssize.height-1);
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const uchar* S = src.data + sy*src.step;
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const uchar* S2 = S - 2;
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#pragma unroll(4)
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for(x = 0; x < avxWidth; x += 16)
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{
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const __m256i CV_DECL_ALIGNED(64) *addr = (__m256i*)(x_ofs + x);
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__m256i CV_DECL_ALIGNED(64) indices = _mm256_lddqu_si256(addr);
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__m256i CV_DECL_ALIGNED(64) pixels1 = _mm256_i32gather_epi32((const int*)S, indices, 1);
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const __m256i CV_DECL_ALIGNED(64) *addr2 = (__m256i*)(x_ofs + x + 8);
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__m256i CV_DECL_ALIGNED(64) indices2 = _mm256_lddqu_si256(addr2);
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__m256i CV_DECL_ALIGNED(64) pixels2 = _mm256_i32gather_epi32((const int*)S2, indices2, 1);
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__m256i CV_DECL_ALIGNED(64) unpacked = _mm256_blend_epi16(pixels1, pixels2, 0xaa);
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__m256i CV_DECL_ALIGNED(64) bytes_shuffled = _mm256_shuffle_epi8(unpacked, shuffle_mask);
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__m256i CV_DECL_ALIGNED(64) ints_permuted = _mm256_permutevar8x32_epi32(bytes_shuffled, permute_mask);
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_mm256_maskstore_epi32((int*)D, mask, ints_permuted);
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D += 32;
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}
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for(; x < width; x++)
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{
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*(ushort*)(Dstart + x*2) = *(ushort*)(S + x_ofs[x]);
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}
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}
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}
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else
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{
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for(y = range.start; y < range.end; y++)
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{
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uchar* D = dst.data + dst.step*y;
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uchar* Dstart = D;
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int sy = std::min(cvFloor(y*ify), ssize.height-1);
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const uchar* S = src.data + sy*src.step;
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const uchar* S2 = S - 2;
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#pragma unroll(4)
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for(x = 0; x < avxWidth; x += 16)
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{
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const __m256i CV_DECL_ALIGNED(64) *addr = (__m256i*)(x_ofs + x);
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__m256i CV_DECL_ALIGNED(64) indices = _mm256_lddqu_si256(addr);
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__m256i CV_DECL_ALIGNED(64) pixels1 = _mm256_i32gather_epi32((const int*)S, indices, 1);
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const __m256i CV_DECL_ALIGNED(64) *addr2 = (__m256i*)(x_ofs + x + 8);
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__m256i CV_DECL_ALIGNED(64) indices2 = _mm256_lddqu_si256(addr2);
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__m256i CV_DECL_ALIGNED(64) pixels2 = _mm256_i32gather_epi32((const int*)S2, indices2, 1);
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__m256i CV_DECL_ALIGNED(64) unpacked = _mm256_blend_epi16(pixels1, pixels2, 0xaa);
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__m256i CV_DECL_ALIGNED(64) bytes_shuffled = _mm256_shuffle_epi8(unpacked, shuffle_mask);
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__m256i CV_DECL_ALIGNED(64) ints_permuted = _mm256_permutevar8x32_epi32(bytes_shuffled, permute_mask);
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_mm256_storeu_si256((__m256i*)D, ints_permuted);
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D += 32;
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}
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for(; x < width; x++)
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{
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*(ushort*)(Dstart + x*2) = *(ushort*)(S + x_ofs[x]);
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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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const Mat src;
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Mat dst;
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int* x_ofs, pix_size4;
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double ify;
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resizeNNInvokerAVX2(const resizeNNInvokerAVX2&);
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resizeNNInvokerAVX2& operator=(const resizeNNInvokerAVX2&);
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};
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#endif
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#if CV_SSE4_1
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class resizeNNInvokerSSE2 :
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public ParallelLoopBody
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{
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public:
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resizeNNInvokerSSE2(const Mat& _src, Mat &_dst, int *_x_ofs, int _pix_size4, double _ify) :
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ParallelLoopBody(), src(_src), dst(_dst), x_ofs(_x_ofs), pix_size4(_pix_size4),
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ify(_ify)
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{
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}
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#pragma optimization_parameter target_arch=SSE4.2
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virtual void operator() (const Range& range) const
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{
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Size ssize = src.size(), dsize = dst.size();
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int y, x, pix_size = (int)src.elemSize();
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int width = dsize.width;
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int sseWidth = width - (width & 0x7);
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for(y = range.start; y < range.end; y++)
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{
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uchar* D = dst.data + dst.step*y;
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uchar* Dstart = D;
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int sy = std::min(cvFloor(y*ify), ssize.height-1);
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const uchar* S = src.data + sy*src.step;
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__m128i CV_DECL_ALIGNED(64) pixels = _mm_set1_epi16(0);
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for(x = 0; x < sseWidth; x += 8)
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{
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ushort imm = *(ushort*)(S + x_ofs[x + 0]);
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pixels = _mm_insert_epi16(pixels, imm, 0);
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imm = *(ushort*)(S + x_ofs[x + 1]);
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pixels = _mm_insert_epi16(pixels, imm, 1);
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imm = *(ushort*)(S + x_ofs[x + 2]);
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pixels = _mm_insert_epi16(pixels, imm, 2);
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imm = *(ushort*)(S + x_ofs[x + 3]);
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pixels = _mm_insert_epi16(pixels, imm, 3);
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imm = *(ushort*)(S + x_ofs[x + 4]);
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pixels = _mm_insert_epi16(pixels, imm, 4);
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imm = *(ushort*)(S + x_ofs[x + 5]);
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pixels = _mm_insert_epi16(pixels, imm, 5);
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imm = *(ushort*)(S + x_ofs[x + 6]);
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pixels = _mm_insert_epi16(pixels, imm, 6);
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imm = *(ushort*)(S + x_ofs[x + 7]);
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pixels = _mm_insert_epi16(pixels, imm, 7);
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_mm_storeu_si128((__m128i*)D, pixels);
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D += 16;
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}
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for(; x < width; x++)
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{
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*(ushort*)(Dstart + x*2) = *(ushort*)(S + x_ofs[x]);
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}
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}
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}
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private:
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const Mat src;
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Mat dst;
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int* x_ofs, pix_size4;
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double ify;
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resizeNNInvokerSSE2(const resizeNNInvokerSSE2&);
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resizeNNInvokerSSE2& operator=(const resizeNNInvokerSSE2&);
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};
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class resizeNNInvokerSSE4 :
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public ParallelLoopBody
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{
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public:
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resizeNNInvokerSSE4(const Mat& _src, Mat &_dst, int *_x_ofs, int _pix_size4, double _ify) :
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ParallelLoopBody(), src(_src), dst(_dst), x_ofs(_x_ofs), pix_size4(_pix_size4),
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ify(_ify)
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{
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}
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#pragma optimization_parameter target_arch=SSE4.2
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virtual void operator() (const Range& range) const
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{
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Size ssize = src.size(), dsize = dst.size();
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int y, x, pix_size = (int)src.elemSize();
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int width = dsize.width;
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int sseWidth = width - (width & 0x3);
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for(y = range.start; y < range.end; y++)
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{
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uchar* D = dst.data + dst.step*y;
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uchar* Dstart = D;
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int sy = std::min(cvFloor(y*ify), ssize.height-1);
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const uchar* S = src.data + sy*src.step;
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__m128i CV_DECL_ALIGNED(64) pixels = _mm_set1_epi16(0);
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for(x = 0; x < sseWidth; x += 4)
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{
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int imm = *(int*)(S + x_ofs[x + 0]);
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pixels = _mm_insert_epi32(pixels, imm, 0);
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imm = *(int*)(S + x_ofs[x + 1]);
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pixels = _mm_insert_epi32(pixels, imm, 1);
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imm = *(int*)(S + x_ofs[x + 2]);
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pixels = _mm_insert_epi32(pixels, imm, 2);
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imm = *(int*)(S + x_ofs[x + 3]);
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pixels = _mm_insert_epi32(pixels, imm, 3);
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_mm_storeu_si128((__m128i*)D, pixels);
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D += 16;
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}
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for(; x < width; x++)
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{
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*(int*)(Dstart + x*4) = *(int*)(S + x_ofs[x]);
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}
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}
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}
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private:
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const Mat src;
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Mat dst;
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int* x_ofs, pix_size4;
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double ify;
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resizeNNInvokerSSE4(const resizeNNInvokerSSE4&);
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resizeNNInvokerSSE4& operator=(const resizeNNInvokerSSE4&);
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};
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#endif
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static void
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resizeNN( const Mat& src, Mat& dst, double fx, double fy )
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{
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@ -435,8 +729,42 @@ resizeNN( const Mat& src, Mat& dst, double fx, double fy )
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}
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Range range(0, dsize.height);
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resizeNNInvoker invoker(src, dst, x_ofs, pix_size4, ify);
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parallel_for_(range, invoker, dst.total()/(double)(1<<16));
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#if CV_AVX2
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if(checkHardwareSupport(CV_CPU_AVX2) && ((pix_size == 2) || (pix_size == 4)))
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{
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if(pix_size == 2)
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{
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resizeNNInvokerAVX2 invoker(src, dst, x_ofs, pix_size4, ify);
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parallel_for_(range, invoker, dst.total()/(double)(1<<16));
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}
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else if (pix_size == 4)
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{
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resizeNNInvokerAVX4 invoker(src, dst, x_ofs, pix_size4, ify);
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parallel_for_(range, invoker, dst.total()/(double)(1<<16));
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}
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}
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else
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#endif
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#if CV_SSE4_1
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if(checkHardwareSupport(CV_CPU_SSE4_1) && ((pix_size == 2) || (pix_size == 4)))
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{
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if(pix_size == 2)
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{
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resizeNNInvokerSSE2 invoker(src, dst, x_ofs, pix_size4, ify);
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parallel_for_(range, invoker, dst.total()/(double)(1<<16));
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}
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else if(pix_size == 4)
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{
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resizeNNInvokerSSE4 invoker(src, dst, x_ofs, pix_size4, ify);
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parallel_for_(range, invoker, dst.total()/(double)(1<<16));
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}
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}
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else
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#endif
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{
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resizeNNInvoker invoker(src, dst, x_ofs, pix_size4, ify);
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parallel_for_(range, invoker, dst.total()/(double)(1<<16));
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}
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}
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