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93 lines
3.3 KiB
93 lines
3.3 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include <unordered_map>
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#include <vector>
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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template <typename Place, typename T>
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class PositiveNegativePairKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto score_t = context.Input<Tensor>("Score");
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auto label_t = context.Input<Tensor>("Label");
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auto query_t = context.Input<Tensor>("QueryId");
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auto positive_t = context.Output<Tensor>("PositivePair");
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auto negative_t = context.Output<Tensor>("NegativePair");
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auto neutral_t = context.Output<Tensor>("NeutralPair");
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auto score = score_t->data<float>();
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auto label = label_t->data<float>();
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auto query = query_t->data<int>();
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T* positive = positive_t->mutable_data<T>(context.GetPlace());
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T* negative = negative_t->mutable_data<T>(context.GetPlace());
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T* neutral = neutral_t->mutable_data<T>(context.GetPlace());
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auto score_dim = score_t->dims();
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PADDLE_ENFORCE_GE(score_dim.size(), 1L,
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"Rank of Score must be at least 1.");
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PADDLE_ENFORCE_LE(score_dim.size(), 2L,
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"Rank of Score must be less or equal to 2.");
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auto batch_size = score_dim[0];
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auto width = score_dim.size() > 1 ? score_dim[1] : 1;
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// construct document instances for each query: Query => List[<score#0,
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// label#0>, ...]
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std::unordered_map<int, std::vector<std::pair<float, float>>> predictions;
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for (auto i = 0; i < batch_size; ++i) {
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if (predictions.find(query[i]) == predictions.end()) {
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predictions.emplace(
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std::make_pair(query[i], std::vector<std::pair<float, float>>()));
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}
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predictions[query[i]].push_back(
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std::make_pair(score[i * width + width - 1], label[i]));
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}
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// for each query, accumulate pair counts
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T pos = 0, neg = 0, neu = 0;
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auto evaluate_one_list = [&pos, &neg,
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&neu](std::vector<std::pair<float, float>> vec) {
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for (auto ite1 = vec.begin(); ite1 != vec.end(); ++ite1) {
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for (auto ite2 = ite1 + 1; ite2 != vec.end(); ++ite2) {
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if (ite1->second == ite2->second) { // labels are equal, ignore.
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continue;
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}
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if (ite1->first == ite2->first) {
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++neu;
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}
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(ite1->first - ite2->first) * (ite1->second - ite2->second) > 0.0
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? pos++
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: neg++;
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}
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}
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};
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for (auto prediction : predictions) {
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evaluate_one_list(prediction.second);
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}
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*positive = pos;
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*negative = neg;
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*neutral = neu;
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}
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};
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} // namespace operators
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} // namespace paddle
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