// Copyright (c) 2021 Mobvoi Inc (Zhendong Peng) // // 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 "wn_utils.h" #include "log.h" #include #include #include #include #include #include namespace wenet { float LogAdd(float x, float y) { static float num_min = -std::numeric_limits::max(); if (x <= num_min) return y; if (y <= num_min) return x; float xmax = std::max(x, y); return std::log(std::exp(x - xmax) + std::exp(y - xmax)) + xmax; } template struct ValueComp { bool operator()(const std::pair& lhs, const std::pair& rhs) const { return lhs.first > rhs.first || (lhs.first == rhs.first && lhs.second < rhs.second); } }; // We refer the pytorch topk implementation // https://github.com/pytorch/pytorch/blob/master/caffe2/operators/top_k.cc template void TopK(const std::vector& data, int32_t k, std::vector* values, std::vector* indices) { std::vector> heap_data; int n = data.size(); for (int32_t i = 0; i < k && i < n; ++i) { heap_data.emplace_back(data[i], i); } std::priority_queue, std::vector>, ValueComp> pq(ValueComp(), std::move(heap_data)); for (int32_t i = k; i < n; ++i) { if (pq.top().first < data[i]) { pq.pop(); pq.emplace(data[i], i); } } values->resize(std::min(k, n)); indices->resize(std::min(k, n)); int32_t cur = values->size() - 1; while (!pq.empty()) { const auto& item = pq.top(); (*values)[cur] = item.first; (*indices)[cur] = item.second; pq.pop(); cur -= 1; } } template void TopK(const std::vector& data, int32_t k, std::vector* values, std::vector* indices); } // namespace wenet