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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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#include "paddle/operators/auc_op.h"
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namespace paddle {
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namespace operators {
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class AccuracyOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Inference"),
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"Input of Inference must be initialized.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
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"Input of Inference must be initialized.");
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auto *inference = ctx.Input<framework::Tensor>("Inference");
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auto *inference_prob = ctx.Input<framework::Tensor>("InferenceProb");
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auto *label = ctx.Input<framework::Tensor>("Label");
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PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label must be a vector");
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PADDLE_ENFORCE_EQ(inference->dims()[0], label->dims()[0],
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"inference size must be the same as label size");
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PADDLE_ENFORCE_EQ(inference->dims(), inference_prob->dims());
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ctx.Output<Tensor>("Accuracy")->Resize({1});
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}
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};
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class AucOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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AucOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Inference",
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"Topk(indices) the network output, float value indicating "
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"probabilities of classification");
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AddInput("InferenceProb",
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"Topk(values) the network output, float value indicating "
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"probabilities of classification");
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AddInput("Label", "Label of the training data");
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// TODO(typhoonzero): support weight
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AddOutput("AUC", "Area Under Curve caculations");
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AddAttr<std::string>("curve", "Possible curves are ROC and PR")
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.SetDefault("ROC");
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AddAttr<int>("num_thresholds",
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"The number of thresholds to use when discretizing the"
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" roc curve.")
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.SetDefault(200);
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AddComment(
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R"DOC(Computes the AUC according forward output and label.
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You can find the definations here:
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https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve
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Possible curves are:
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ROC: Receiver operating characteristic
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PR: Precision Recall
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_WITHOUT_GRADIENT(auc, ops::AccuracyOp, ops::AccuracyOpMaker);
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REGISTER_OP_CPU_KERNEL(auc, ops::AucKernel<paddle::platform::CPUPlace, float>);
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@ -0,0 +1,132 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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 <algorithm>
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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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template <typename Place, typename T>
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class AccuracyKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* inference = ctx.Input<Tensor>("Inference");
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auto* inference_prob = ctx.Input<Tensor>("InferenceProb");
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auto* label = ctx.Input<Tensor>("Label");
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auto* auc = ctx.Output<Tensor>("AUC");
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float* auc_data = auc->mutable_data<float>(ctx.GetPlace());
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std::string curve = ctx.Attr<std::string>("curve");
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int num_thresholds = ctx.Attr<int>("num_thresholds");
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std::vector<float> thresholds_list;
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thresholds_list.reserve(num_thresholds);
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for (int i = 1; i < num_thresholds - 1; i++) {
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thresholds_list[i] = (float)i / (num_thresholds - 1);
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}
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const float kEpsilon = 1e-7;
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thresholds_list[0] = 0.0f - kEpsilon;
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thresholds_list[num_thresholds - 1] = 1.0f + kEpsilon;
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const int* inference_data = inference->data<int>();
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const T* inference_prob_data = inference->data<T>();
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const T* label_data = label->data<T>();
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size_t num_samples = inference->dims()[0];
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size_t class_dim = inference->dims()[1];
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// create local tensor for storing the curve: TP, FN, TN, FP
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// TODO(typhoonzero): put these tensors in Scope
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// TODO(typhoonzero): use op to caculate these values.
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Tensor true_positive, false_positeve, true_negative, false_negative;
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true_positive.Resize({num_thresholds});
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false_negative.Resize({num_thresholds});
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true_negative.Resize({num_thresholds});
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false_positive.Resize({num_thresholds});
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int* tp_data = true_positive.mutable_data<int>();
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int* fn_data = false_negative.mutable_data<int>();
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int* tn_data = true_negative.mutable_data<int>();
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int* fp_data = false_positive.mutable_data<int>();
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for (auto thresh = thresholds_list.begin(); thresh != thresholds_list.end();
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thresh++) {
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size_t idx_thresh = thresh - thresholds_list.begin();
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// caculate TP, FN, TN, FP for current thresh
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int tp, fn, tn, fp = 0;
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for (size_t i = 0; i < num_samples; i++) {
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for (size_t j = 0; j < class_dim; j++) {
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if (inference_data[i * class_dim + j] == label_data[i]) {
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if (inference_prob_data[i * class_dim + j] >= (*thresh)) {
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tp++;
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} else {
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tn++;
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}
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} else {
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if (inference_prob_data[i * class_dim + j] >= (*thresh)) {
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fp++;
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} else {
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fn++;
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}
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}
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}
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}
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// store rates
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tp_data[idx_thresh] = tp;
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fn_data[idx_thresh] = fn;
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tn_data[idx_thresh] = tn;
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fp_data[idx_thresh] = fp;
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}
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// epsilon to avoid divide by zero.
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float epsilon = 1e-6;
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// Riemann sum to caculate auc.
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Tensor tp_rate, fp_rate, rec_rate;
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tp_rate.Resize({num_thresholds});
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fp_rate.Resize({num_thresholds});
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rec_rate.Resize({num_thresholds});
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float* tp_rate_data = tp_rate.mutable_data<float>();
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float* fp_rate_data = fp_rate.mutable_data<float>();
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float* rec_rate_data = rec_rate.mutable_data<float>();
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for (int i = 0; i < num_thresholds; i++) {
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tp_rate_data[i] = ((float)tp_data[i + epsilon) / (tp_data[i] + fn_data[i] + epsilon);
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fp_rate_data[i] =
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(float)fp_data[i] / (fp_data[i] + tn_data[i] + epsilon);
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rec_rate_data[i] =
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((float)tp_data[i] + epsilon) / (tp_data[i] + fp_data[i] + epsilon);
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}
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if (curve == "ROC") {
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for (int i = 1; i < num_thresholds; i++) {
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auto dx = fp_rate_data[i] - fp_rate_data[i - 1];
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auto y = (tp_rate_data[i] + tp_rate_data[i - 1]) / 2.0f;
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*auc_data = *auc_data + dx * y;
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}
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} else if (curve = "PR") {
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for (int i = 1; i < num_thresholds; i++) {
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auto dx = tp_rate_data[i] - tp_rate_data[i - 1];
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auto y = (rec_rate_data[i] + rec_rate_data[i - 1]) / 2.0f;
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*auc_data = *auc_data + dx * y;
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}
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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