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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.
-
- #include "scale.h"
-
- namespace ncnn {
-
- DEFINE_LAYER_CREATOR(Scale)
-
- Scale::Scale()
- {
- one_blob_only = true;
- support_inplace = true;
- }
-
- int Scale::load_param(const ParamDict& pd)
- {
- scale_data_size = pd.get(0, 0);
- bias_term = pd.get(1, 0);
-
- if (scale_data_size == -233)
- one_blob_only = false;
-
- return 0;
- }
-
- #if NCNN_STDIO
- int Scale::load_model(FILE* binfp)
- {
- int nread;
-
- if (scale_data_size != -233)
- {
- scale_data.create(scale_data_size);
- if (scale_data.empty())
- return -100;
- nread = fread(scale_data, scale_data_size * sizeof(float), 1, binfp);
- if (nread != 1)
- {
- fprintf(stderr, "Scale read scale_data failed %d\n", nread);
- return -1;
- }
- }
-
- if (bias_term)
- {
- bias_data.create(scale_data_size);
- if (bias_data.empty())
- return -100;
- nread = fread(bias_data, scale_data_size * sizeof(float), 1, binfp);
- if (nread != 1)
- {
- fprintf(stderr, "Scale read bias_data failed %d\n", nread);
- return -1;
- }
- }
-
- return 0;
- }
- #endif // NCNN_STDIO
-
- int Scale::load_model(const unsigned char*& mem)
- {
- if (scale_data_size != -233)
- {
- scale_data = Mat(scale_data_size, (float*)mem);
- mem += scale_data_size * sizeof(float);
- }
-
- if (bias_term)
- {
- bias_data = Mat(scale_data_size, (float*)mem);
- mem += scale_data_size * sizeof(float);
- }
-
- return 0;
- }
-
- int Scale::forward_inplace(std::vector<Mat>& bottom_top_blobs) const
- {
- Mat& bottom_top_blob = bottom_top_blobs[0];
- const Mat& scale_blob = bottom_top_blobs[1];
-
- int w = bottom_top_blob.w;
- int h = bottom_top_blob.h;
- int channels = bottom_top_blob.c;
- int size = w * h;
-
- if (bias_term)
- {
- const float* bias_ptr = bias_data;
- #pragma omp parallel for
- for (int q=0; q<channels; q++)
- {
- float* ptr = bottom_top_blob.channel(q);
-
- float s = scale_blob.channel(q)[0];
- float bias = bias_ptr[q];
-
- for (int i=0; i<size; i++)
- {
- ptr[i] = ptr[i] * s + bias;
- }
- }
- }
- else
- {
- #pragma omp parallel for
- for (int q=0; q<channels; q++)
- {
- float* ptr = bottom_top_blob.channel(q);
-
- float s = scale_blob.channel(q)[0];
-
- for (int i=0; i<size; i++)
- {
- ptr[i] *= s;
- }
- }
- }
-
- return 0;
- }
-
- int Scale::forward_inplace(Mat& bottom_top_blob) const
- {
- int w = bottom_top_blob.w;
- int h = bottom_top_blob.h;
- int channels = bottom_top_blob.c;
- int size = w * h;
-
- if (bias_term)
- {
- const float* scale_ptr = scale_data;
- const float* bias_ptr = bias_data;
- #pragma omp parallel for
- for (int q=0; q<channels; q++)
- {
- float* ptr = bottom_top_blob.channel(q);
-
- float s = scale_ptr[q];
- float bias = bias_ptr[q];
-
- for (int i=0; i<size; i++)
- {
- ptr[i] = ptr[i] * s + bias;
- }
- }
- }
- else
- {
- const float* scale_ptr = scale_data;
- #pragma omp parallel for
- for (int q=0; q<channels; q++)
- {
- float* ptr = bottom_top_blob.channel(q);
-
- float s = scale_ptr[q];
-
- for (int i=0; i<size; i++)
- {
- ptr[i] *= s;
- }
- }
- }
-
- return 0;
- }
-
- } // namespace ncnn
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