File size: 3,686 Bytes
be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | // Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.
static void convolution_pack4_msa(const Mat& bottom_blob, Mat& top_blob, const Mat& weight_data_pack4, const Mat& bias_data, int kernel_w, int kernel_h, int dilation_w, int dilation_h, int stride_w, int stride_h, int activation_type, const Mat& activation_params, 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;
// kernel offsets
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;
}
}
const float* bias_data_ptr = bias_data;
#pragma omp parallel for num_threads(opt.num_threads)
for (int p = 0; p < outch; p++)
{
float* outptr = top_blob.channel(p);
for (int i = 0; i < outh; i++)
{
for (int j = 0; j < outw; j++)
{
v4f32 _sum = (v4f32)__msa_fill_w(0);
if (bias_data_ptr)
{
_sum = (v4f32)__msa_ld_w(bias_data_ptr + p * 4, 0);
}
const float* kptr = (const float*)weight_data_pack4.channel(p);
// channels
for (int q = 0; q < channels; q++)
{
const Mat m = bottom_blob.channel(q);
const float* sptr = m.row(i * stride_h) + j * stride_w * 4;
for (int k = 0; k < maxk; k++) // 29.23
{
const float* slptr = sptr + space_ofs[k] * 4;
v4f32 _val0 = __msa_fill_w_f32(slptr[0]);
v4f32 _val1 = __msa_fill_w_f32(slptr[1]);
v4f32 _val2 = __msa_fill_w_f32(slptr[2]);
v4f32 _val3 = __msa_fill_w_f32(slptr[3]);
v4f32 _w0 = (v4f32)__msa_ld_w(kptr, 0);
v4f32 _w1 = (v4f32)__msa_ld_w(kptr + 4, 0);
v4f32 _w2 = (v4f32)__msa_ld_w(kptr + 8, 0);
v4f32 _w3 = (v4f32)__msa_ld_w(kptr + 12, 0);
_sum = __msa_fmadd_w(_sum, _val0, _w0);
_sum = __msa_fmadd_w(_sum, _val1, _w1);
_sum = __msa_fmadd_w(_sum, _val2, _w2);
_sum = __msa_fmadd_w(_sum, _val3, _w3);
kptr += 16;
}
}
_sum = activation_ps(_sum, activation_type, activation_params);
__msa_st_w((v4i32)_sum, outptr + j * 4, 0);
}
outptr += outw * 4;
}
}
}
|