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| | static void convolution_int8(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++) |
| | { |
| | int sum = 0; |
| |
|
| | |
| | const signed char* kptr = (const signed char*)weight_data_int8 + maxk * channels * p; |
| |
|
| | |
| | for (int q = 0; q < channels; q++) |
| | { |
| | const Mat m = bottom_blob.channel(q); |
| | const signed char* sptr = m.row<signed char>(i * stride_h) + j * stride_w; |
| |
|
| | for (int k = 0; k < maxk; k++) |
| | { |
| | signed char val = sptr[space_ofs[k]]; |
| | signed char w = kptr[k]; |
| | sum += val * w; |
| | } |
| |
|
| | kptr += maxk; |
| | } |
| |
|
| | outptr[j] = sum; |
| | } |
| |
|
| | outptr += outw; |
| | } |
| | } |
| | } |
| |
|