mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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49 lines
1.6 KiB
C++
49 lines
1.6 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/vision/utils/utils.h"
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namespace fastdeploy {
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namespace vision {
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namespace utils {
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float CosineSimilarity(const std::vector<float>& a, const std::vector<float>& b,
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bool normalized) {
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FDASSERT((a.size() == b.size()) && (a.size() != 0),
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"The size of a and b must be equal and >= 1.");
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size_t num_val = a.size();
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if (normalized) {
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float mul_a = 0.f, mul_b = 0.f, mul_ab = 0.f;
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for (size_t i = 0; i < num_val; ++i) {
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mul_a += (a[i] * a[i]);
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mul_b += (b[i] * b[i]);
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mul_ab += (a[i] * b[i]);
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}
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return (mul_ab / (std::sqrt(mul_a) * std::sqrt(mul_b)));
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}
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auto norm_a = L2Normalize(a);
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auto norm_b = L2Normalize(b);
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float mul_a = 0.f, mul_b = 0.f, mul_ab = 0.f;
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for (size_t i = 0; i < num_val; ++i) {
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mul_a += (norm_a[i] * norm_a[i]);
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mul_b += (norm_b[i] * norm_b[i]);
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mul_ab += (norm_a[i] * norm_b[i]);
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}
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return (mul_ab / (std::sqrt(mul_a) * std::sqrt(mul_b)));
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}
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} // namespace utils
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} // namespace vision
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} // namespace fastdeploy
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