| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| |
|
| | static void convolution_pack1to4_int8_msa(const Mat& bottom_blob, Mat& top_blob, const Mat& weight_data_int8, int kernel_w, int kernel_h, int dilation_w, int dilation_h, int stride_w, int stride_h, const Option& opt) |
| | { |
| | int w = bottom_blob.w; |
| | int channels = bottom_blob.c; |
| |
|
| | int outw = top_blob.w; |
| | int outh = top_blob.h; |
| | int outch = top_blob.c; |
| |
|
| | const int maxk = kernel_w * kernel_h; |
| |
|
| | |
| | std::vector<int> _space_ofs(maxk); |
| | int* space_ofs = &_space_ofs[0]; |
| | { |
| | int p1 = 0; |
| | int p2 = 0; |
| | int gap = w * dilation_h - kernel_w * dilation_w; |
| | for (int i = 0; i < kernel_h; i++) |
| | { |
| | for (int j = 0; j < kernel_w; j++) |
| | { |
| | space_ofs[p1] = p2; |
| | p1++; |
| | p2 += dilation_w; |
| | } |
| | p2 += gap; |
| | } |
| | } |
| |
|
| | |
| | #pragma omp parallel for num_threads(opt.num_threads) |
| | for (int p = 0; p < outch; p++) |
| | { |
| | int* outptr = top_blob.channel(p); |
| |
|
| | for (int i = 0; i < outh; i++) |
| | { |
| | for (int j = 0; j < outw; j++) |
| | { |
| | v4i32 _sum = __msa_fill_w(0); |
| |
|
| | const signed char* kptr = weight_data_int8.channel(p); |
| |
|
| | |
| | for (int q = 0; q < channels; q++) |
| | { |
| | const Mat m = bottom_blob.channel(q); |
| | const signed char* sptr = m.row<const signed char>(i * stride_h) + j * stride_w; |
| |
|
| | for (int k = 0; k < maxk; k++) |
| | { |
| | v8i16 _val = __msa_fill_h((short)sptr[space_ofs[k]]); |
| |
|
| | v16i8 _w = __msa_ld_b(kptr, 0); |
| | v8i16 _w16 = (v8i16)__msa_ilvr_b(__msa_clti_s_b(_w, 0), _w); |
| |
|
| | v8i16 _s0 = __msa_mulv_h(_val, _w16); |
| | v4i32 _s032 = (v4i32)__msa_ilvr_h(__msa_clti_s_h(_s0, 0), _s0); |
| |
|
| | _sum = __msa_addv_w(_sum, _s032); |
| |
|
| | kptr += 4; |
| | } |
| | } |
| |
|
| | __msa_st_w(_sum, outptr + j * 4, 0); |
| | } |
| |
|
| | outptr += outw * 4; |
| | } |
| | } |
| | } |
| |
|