mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 00:57:33 +08:00
42 lines
1.2 KiB
C++
42 lines
1.2 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// 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 "fastdeploy/vision/utils/utils.h"
|
|
|
|
namespace fastdeploy {
|
|
namespace vision {
|
|
namespace utils {
|
|
|
|
std::vector<float> L2Normalize(const std::vector<float>& values) {
|
|
size_t num_val = values.size();
|
|
if (num_val == 0) {
|
|
return {};
|
|
}
|
|
std::vector<float> norm;
|
|
float l2_sum_val = 0.f;
|
|
for (size_t i = 0; i < num_val; ++i) {
|
|
l2_sum_val += (values[i] * values[i]);
|
|
}
|
|
float l2_sum_sqrt = std::sqrt(l2_sum_val);
|
|
norm.resize(num_val);
|
|
for (size_t i = 0; i < num_val; ++i) {
|
|
norm[i] = values[i] / l2_sum_sqrt;
|
|
}
|
|
return norm;
|
|
}
|
|
|
|
} // namespace utils
|
|
} // namespace vision
|
|
} // namespace fastdeploy
|