// 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& 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