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| 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; |
|
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| int outw = top_blob.w; |
| int outh = top_blob.h; |
| int outch = top_blob.c; |
|
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| const int maxk = kernel_w * kernel_h; |
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| |
| 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; |
| } |
| } |
|
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| |
| #pragma omp parallel for num_threads(opt.num_threads) |
| for (int p = 0; p < outch; p++) |
| { |
| int* outptr = top_blob.channel(p); |
|
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| for (int i = 0; i < outh; i++) |
| { |
| for (int j = 0; j < outw; j++) |
| { |
| v4i32 _sum = __msa_fill_w(0); |
|
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| const signed char* kptr = weight_data_int8.channel(p); |
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| |
| 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; |
|
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| for (int k = 0; k < maxk; k++) |
| { |
| v8i16 _val = __msa_fill_h((short)sptr[space_ofs[k]]); |
|
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| 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); |
|
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| _sum = __msa_addv_w(_sum, _s032); |
|
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| kptr += 4; |
| } |
| } |
|
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| __msa_st_w(_sum, outptr + j * 4, 0); |
| } |
|
|
| outptr += outw * 4; |
| } |
| } |
| } |
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|