PaddleX/deploy/ultra-infer/ultra_infer/vision/utils/cosine_similarity.cc

// 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 "ultra_infer/vision/utils/utils.h"

namespace ultra_infer {
namespace vision {
namespace utils {

float CosineSimilarity(const std::vector<float> &a, const std::vector<float> &b,
                       bool normalized) {
  FDASSERT((a.size() == b.size()) && (a.size() != 0),
           "The size of a and b must be equal and >= 1.");
  size_t num_val = a.size();
  if (normalized) {
    float mul_a = 0.f, mul_b = 0.f, mul_ab = 0.f;
    for (size_t i = 0; i < num_val; ++i) {
      mul_a += (a[i] * a[i]);
      mul_b += (b[i] * b[i]);
      mul_ab += (a[i] * b[i]);
    }
    return (mul_ab / (std::sqrt(mul_a) * std::sqrt(mul_b)));
  }
  auto norm_a = L2Normalize(a);
  auto norm_b = L2Normalize(b);
  float mul_a = 0.f, mul_b = 0.f, mul_ab = 0.f;
  for (size_t i = 0; i < num_val; ++i) {
    mul_a += (norm_a[i] * norm_a[i]);
    mul_b += (norm_b[i] * norm_b[i]);
    mul_ab += (norm_a[i] * norm_b[i]);
  }
  return (mul_ab / (std::sqrt(mul_a) * std::sqrt(mul_b)));
}

} // namespace utils
} // namespace vision
} // namespace ultra_infer