// 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.

#include "batchnorm.h"

namespace ncnn {

BatchNorm::BatchNorm()
{
    one_blob_only = true;
    support_inplace = true;
}

int BatchNorm::load_param(const ParamDict& pd)
{
    channels = pd.get(0, 0);
    eps = pd.get(1, 0.f);

    return 0;
}

int BatchNorm::load_model(const ModelBin& mb)
{
    slope_data = mb.load(channels, 1);
    if (slope_data.empty())
        return -100;

    mean_data = mb.load(channels, 1);
    if (mean_data.empty())
        return -100;

    var_data = mb.load(channels, 1);
    if (var_data.empty())
        return -100;

    bias_data = mb.load(channels, 1);
    if (bias_data.empty())
        return -100;

    a_data.create(channels);
    if (a_data.empty())
        return -100;
    b_data.create(channels);
    if (b_data.empty())
        return -100;

    for (int i = 0; i < channels; i++)
    {
        float sqrt_var = sqrtf(var_data[i] + eps);
        if (sqrt_var == 0.f)
            sqrt_var = 0.0001f; // sanitize divide by zero
        a_data[i] = bias_data[i] - slope_data[i] * mean_data[i] / sqrt_var;
        b_data[i] = slope_data[i] / sqrt_var;
    }

    return 0;
}

int BatchNorm::forward_inplace(Mat& bottom_top_blob, const Option& opt) const
{
    // a = bias - slope * mean / sqrt(var)
    // b = slope / sqrt(var)
    // value = b * value + a

    int dims = bottom_top_blob.dims;

    if (dims == 1)
    {
        int w = bottom_top_blob.w;

        float* ptr = bottom_top_blob;

        #pragma omp parallel for num_threads(opt.num_threads)
        for (int i = 0; i < w; i++)
        {
            ptr[i] = b_data[i] * ptr[i] + a_data[i];
        }
    }

    if (dims == 2)
    {
        int w = bottom_top_blob.w;
        int h = bottom_top_blob.h;

        #pragma omp parallel for num_threads(opt.num_threads)
        for (int i = 0; i < h; i++)
        {
            float* ptr = bottom_top_blob.row(i);
            float a = a_data[i];
            float b = b_data[i];

            for (int j = 0; j < w; j++)
            {
                ptr[j] = b * ptr[j] + a;
            }
        }
    }

    if (dims == 3 || dims == 4)
    {
        int w = bottom_top_blob.w;
        int h = bottom_top_blob.h;
        int d = bottom_top_blob.d;
        int c = bottom_top_blob.c;
        int size = w * h * d;

        #pragma omp parallel for num_threads(opt.num_threads)
        for (int q = 0; q < c; q++)
        {
            float* ptr = bottom_top_blob.channel(q);
            float a = a_data[q];
            float b = b_data[q];

            for (int i = 0; i < size; i++)
            {
                ptr[i] = b * ptr[i] + a;
            }
        }
    }

    return 0;
}

} // namespace ncnn