ncnn/src/layer/x86/convolution_1x1.h

// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2017 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 conv1x1s1_sse(const Mat& bottom_blob, Mat& top_blob, const Mat& _kernel, const Mat& _bias, const Option& opt)
{
    int inch = bottom_blob.c;

    int outw = top_blob.w;
    int outh = top_blob.h;
    int outch = top_blob.c;

    const float* kernel = _kernel;
    const float* bias = _bias;

    #pragma omp parallel for num_threads(opt.num_threads)
    for (int p=0; p<outch; p++)
    {
        Mat out = top_blob.channel(p);

        const float bias0 = bias ? bias[p] : 0.f;

        out.fill(bias0);

        int q = 0;

        for (; q+3<inch; q+=4)
        {
            float* outptr = out;

            const float* img0 = bottom_blob.channel(q);
            const float* img1 = bottom_blob.channel(q+1);
            const float* img2 = bottom_blob.channel(q+2);
            const float* img3 = bottom_blob.channel(q+3);

            const float* kernel0 = kernel + p*inch  + q;
            const float k0 = kernel0[0];
            const float k1 = kernel0[1];
            const float k2 = kernel0[2];
            const float k3 = kernel0[3];

            const float* r0 = img0;
            const float* r1 = img1;
            const float* r2 = img2;
            const float* r3 = img3;

            int size = outw * outh;

            int remain = size;

            for (; remain>0; remain--)
            {
                float sum = *r0 * k0;
                float sum1 = *r1 * k1;
                float sum2 = *r2 * k2;
                float sum3 = *r3 * k3;

                *outptr += sum + sum1 + sum2 + sum3;

                r0++;
                r1++;
                r2++;
                r3++;
                outptr++;
            }

        }

        for (; q<inch; q++)
        {
            float* outptr = out;

            const float* img0 = bottom_blob.channel(q);

            const float* kernel0 = kernel + p*inch  + q;
            const float k0 = kernel0[0];

            const float* r0 = img0;

            int size = outw * outh;

            int remain = size;

            for (; remain>0; remain--)
            {
                float sum = *r0 * k0;

                *outptr += sum;

                r0++;
                outptr++;
            }

        }
    }

}

static void conv1x1s2_sse(const Mat& bottom_blob, Mat& top_blob, const Mat& _kernel, const Mat& _bias, const Option& opt)
{
    int w = bottom_blob.w;
    int inch = bottom_blob.c;

    int outw = top_blob.w;
    int outh = top_blob.h;
    int outch = top_blob.c;

    const int tailstep = w - 2*outw + w;

    const float* kernel = _kernel;
    const float* bias = _bias;

    #pragma omp parallel for num_threads(opt.num_threads)
    for (int p=0; p<outch; p++)
    {
        Mat out = top_blob.channel(p);

        const float bias0 = bias ? bias[p] : 0.f;

        out.fill(bias0);

        int q = 0;

        for (; q+3<inch; q+=4)
        {
            float* outptr = out;

            const float* img0 = bottom_blob.channel(q);
            const float* img1 = bottom_blob.channel(q+1);
            const float* img2 = bottom_blob.channel(q+2);
            const float* img3 = bottom_blob.channel(q+3);

            const float* kernel0 = kernel + p*inch + q;
            const float k0 = kernel0[0];
            const float k1 = kernel0[1];
            const float k2 = kernel0[2];
            const float k3 = kernel0[3];

            const float* r0 = img0;
            const float* r1 = img1;
            const float* r2 = img2;
            const float* r3 = img3;

            for (int i = 0; i < outh; i++)
            {
                int remain = outw;

                for (; remain>0; remain--)
                {
                    float sum = *r0 * k0;
                    float sum1 = *r1 * k1;
                    float sum2 = *r2 * k2;
                    float sum3 = *r3 * k3;

                    *outptr += sum + sum1 + sum2 + sum3;

                    r0 += 2;
                    r1 += 2;
                    r2 += 2;
                    r3 += 2;
                    outptr++;
                }

                r0 += tailstep;
                r1 += tailstep;
                r2 += tailstep;
                r3 += tailstep;
            }

        }

        for (; q<inch; q++)
        {
            float* outptr = out;

            const float* img0 = bottom_blob.channel(q);

            const float* kernel0 = kernel + p*inch + q;
            const float k0 = kernel0[0];

            const float* r0 = img0;

            for (int i = 0; i < outh; i++)
            {
                int remain = outw;

                for (; remain>0; remain--)
                {
                    float sum = *r0 * k0;

                    *outptr += sum;

                    r0 += 2;
                    outptr++;
                }

                r0 += tailstep;
            }

        }
    }

}