ncnn

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// Tencent is pleased to support the open source community by making ncnn available.
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//
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// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
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//
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// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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// in compliance with the License. You may obtain a copy of the License at
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//
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// https://opensource.org/licenses/BSD-3-Clause
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//
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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#include "bias.h"
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namespace ncnn {
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Bias::Bias()
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{
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    one_blob_only = true;
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    support_inplace = true;
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}
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int Bias::load_param(const ParamDict& pd)
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{
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    bias_data_size = pd.get(0, 0);
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    return 0;
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}
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int Bias::load_model(const ModelBin& mb)
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{
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    bias_data = mb.load(bias_data_size, 1);
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    if (bias_data.empty())
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        return -100;
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    return 0;
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}
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int Bias::forward_inplace(Mat& bottom_top_blob, const Option& opt) const
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{
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    int w = bottom_top_blob.w;
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    int h = bottom_top_blob.h;
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    int d = bottom_top_blob.d;
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    int channels = bottom_top_blob.c;
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    int size = w * h * d;
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    #pragma omp parallel for num_threads(opt.num_threads)
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    for (int q = 0; q < channels; q++)
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    {
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        float* ptr = bottom_top_blob.channel(q);
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        float bias = bias_data[q];
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        for (int i = 0; i < size; i++)
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        {
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            ptr[i] += bias;
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        }
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    }
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    return 0;
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}
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} // namespace ncnn
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