ncnn / src /layer /x86 /convolution_3x3.h

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// 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 conv3x3s1_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 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);

        for (int q = 0; q < inch; q++)
        {
            float* outptr = out;
            float* outptr2 = outptr + outw;

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

            const float* kernel0 = kernel + p * inch * 9 + q * 9;

            const float* r0 = img0;
            const float* r1 = img0 + w;
            const float* r2 = img0 + w * 2;
            const float* r3 = img0 + w * 3;

            const float* k0 = kernel0;
            const float* k1 = kernel0 + 3;
            const float* k2 = kernel0 + 6;

            int i = 0;

            for (; i + 1 < outh; i += 2)
            {
                int remain = outw;

                for (; remain > 0; remain--)
                {
                    float sum = 0;
                    float sum2 = 0;

                    sum += r0[0] * k0[0];
                    sum += r0[1] * k0[1];
                    sum += r0[2] * k0[2];
                    sum += r1[0] * k1[0];
                    sum += r1[1] * k1[1];
                    sum += r1[2] * k1[2];
                    sum += r2[0] * k2[0];
                    sum += r2[1] * k2[1];
                    sum += r2[2] * k2[2];

                    sum2 += r1[0] * k0[0];
                    sum2 += r1[1] * k0[1];
                    sum2 += r1[2] * k0[2];
                    sum2 += r2[0] * k1[0];
                    sum2 += r2[1] * k1[1];
                    sum2 += r2[2] * k1[2];
                    sum2 += r3[0] * k2[0];
                    sum2 += r3[1] * k2[1];
                    sum2 += r3[2] * k2[2];

                    *outptr += sum;
                    *outptr2 += sum2;

                    r0++;
                    r1++;
                    r2++;
                    r3++;
                    outptr++;
                    outptr2++;
                }

                r0 += 2 + w;
                r1 += 2 + w;
                r2 += 2 + w;
                r3 += 2 + w;

                outptr += outw;
                outptr2 += outw;
            }

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

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

                    sum += r0[0] * k0[0];
                    sum += r0[1] * k0[1];
                    sum += r0[2] * k0[2];
                    sum += r1[0] * k1[0];
                    sum += r1[1] * k1[1];
                    sum += r1[2] * k1[2];
                    sum += r2[0] * k2[0];
                    sum += r2[1] * k2[1];
                    sum += r2[2] * k2[2];

                    *outptr += sum;

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

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

static void conv3x3s2_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);

        for (int q = 0; q < inch; q++)
        {
            float* outptr = out;

            const float* img = bottom_blob.channel(q);
            const float* kernel0 = kernel + p * inch * 9 + q * 9;

            const float* r0 = img;
            const float* r1 = img + w;
            const float* r2 = img + w * 2;

            const float* k0 = kernel0;
            const float* k1 = kernel0 + 3;
            const float* k2 = kernel0 + 6;

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

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

                    sum += r0[0] * k0[0];
                    sum += r0[1] * k0[1];
                    sum += r0[2] * k0[2];
                    sum += r1[0] * k1[0];
                    sum += r1[1] * k1[1];
                    sum += r1[2] * k1[2];
                    sum += r2[0] * k2[0];
                    sum += r2[1] * k2[1];
                    sum += r2[2] * k2[2];

                    *outptr += sum;

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

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

	// 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 conv3x3s1_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 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);

	for (int q = 0; q < inch; q++)
	{
	float* outptr = out;
	float* outptr2 = outptr + outw;

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

	const float* kernel0 = kernel + p * inch * 9 + q * 9;

	const float* r0 = img0;
	const float* r1 = img0 + w;
	const float* r2 = img0 + w * 2;
	const float* r3 = img0 + w * 3;

	const float* k0 = kernel0;
	const float* k1 = kernel0 + 3;
	const float* k2 = kernel0 + 6;

	int i = 0;

	for (; i + 1 < outh; i += 2)
	{
	int remain = outw;

	for (; remain > 0; remain--)
	{
	float sum = 0;
	float sum2 = 0;

	sum += r0[0] * k0[0];
	sum += r0[1] * k0[1];
	sum += r0[2] * k0[2];
	sum += r1[0] * k1[0];
	sum += r1[1] * k1[1];
	sum += r1[2] * k1[2];
	sum += r2[0] * k2[0];
	sum += r2[1] * k2[1];
	sum += r2[2] * k2[2];

	sum2 += r1[0] * k0[0];
	sum2 += r1[1] * k0[1];
	sum2 += r1[2] * k0[2];
	sum2 += r2[0] * k1[0];
	sum2 += r2[1] * k1[1];
	sum2 += r2[2] * k1[2];
	sum2 += r3[0] * k2[0];
	sum2 += r3[1] * k2[1];
	sum2 += r3[2] * k2[2];

	*outptr += sum;
	*outptr2 += sum2;

	r0++;
	r1++;
	r2++;
	r3++;
	outptr++;
	outptr2++;
	}

	r0 += 2 + w;
	r1 += 2 + w;
	r2 += 2 + w;
	r3 += 2 + w;

	outptr += outw;
	outptr2 += outw;
	}

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

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

	sum += r0[0] * k0[0];
	sum += r0[1] * k0[1];
	sum += r0[2] * k0[2];
	sum += r1[0] * k1[0];
	sum += r1[1] * k1[1];
	sum += r1[2] * k1[2];
	sum += r2[0] * k2[0];
	sum += r2[1] * k2[1];
	sum += r2[2] * k2[2];

	*outptr += sum;

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

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

	static void conv3x3s2_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);

	for (int q = 0; q < inch; q++)
	{
	float* outptr = out;

	const float* img = bottom_blob.channel(q);
	const float* kernel0 = kernel + p * inch * 9 + q * 9;

	const float* r0 = img;
	const float* r1 = img + w;
	const float* r2 = img + w * 2;

	const float* k0 = kernel0;
	const float* k1 = kernel0 + 3;
	const float* k2 = kernel0 + 6;

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

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

	sum += r0[0] * k0[0];
	sum += r0[1] * k0[1];
	sum += r0[2] * k0[2];
	sum += r1[0] * k1[0];
	sum += r1[1] * k1[1];
	sum += r1[2] * k1[2];
	sum += r2[0] * k2[0];
	sum += r2[1] * k2[1];
	sum += r2[2] * k2[2];

	*outptr += sum;

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

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