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118 lines
4.3 KiB
118 lines
4.3 KiB
/* Copyright (c) 2018 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 <algorithm>
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#include "paddle/fluid/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 T, int D, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
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template <typename T>
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class MeanIoUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto& place = *ctx.template device_context<platform::CPUDeviceContext>()
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.eigen_device();
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// get input and output tensor
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auto* predictions = ctx.Input<Tensor>("Predictions");
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auto* labels = ctx.Input<Tensor>("Labels");
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auto* out_mean_iou = ctx.Output<Tensor>("OutMeanIou");
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auto* out_wrong = ctx.Output<Tensor>("OutWrong");
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auto* out_correct = ctx.Output<Tensor>("OutCorrect");
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int num_classes = static_cast<int>(ctx.Attr<int>("num_classes"));
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// get data ptr
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const T* predictions_data = predictions->data<T>();
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const T* labels_data = labels->data<T>();
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float* out_mean_iou_data =
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out_mean_iou->mutable_data<float>(ctx.GetPlace());
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int* out_wrong_data = out_wrong->mutable_data<int>(ctx.GetPlace());
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int* out_correct_data = out_correct->mutable_data<int>(ctx.GetPlace());
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// get eigen tensor
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auto out_mean_iou_t = EigenTensor<float, 1>::From(*out_mean_iou);
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auto out_wrong_t = EigenTensor<int, 1>::From(*out_wrong);
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auto out_correct_t = EigenTensor<int, 1>::From(*out_correct);
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// Tmp tensor
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Tensor denominator;
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Tensor valid_count;
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Tensor iou_sum;
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// get data ptr of tmp tensor
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int* denominator_data = denominator.mutable_data<int>(
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{static_cast<int64_t>(num_classes)}, ctx.GetPlace());
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int* valid_count_data = valid_count.mutable_data<int>({1}, ctx.GetPlace());
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float* iou_sum_data = iou_sum.mutable_data<float>({1}, ctx.GetPlace());
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// get eigen tensor of tmp tensor
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auto denominator_t = EigenTensor<int, 1>::From(denominator);
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auto valid_count_t = EigenTensor<int, 1>::From(valid_count);
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auto iou_sum_t = EigenTensor<float, 1>::From(iou_sum);
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// init out_wrong, out_correct and out_mean_iou
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out_wrong_t = out_wrong_t.constant(0);
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out_correct_t = out_correct_t.constant(0);
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out_mean_iou_t = out_mean_iou_t.constant(0);
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// collect pre wrong, correct and mean_iou
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auto in_mean_ious = ctx.MultiInput<Tensor>("InMeanIou");
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for (size_t i = 0; i < in_mean_ious.size(); ++i) {
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out_mean_iou_t.device(place) +=
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EigenTensor<float, 1>::From(*in_mean_ious[i]);
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}
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auto in_wrongs = ctx.MultiInput<Tensor>("InWrongs");
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for (size_t i = 0; i < in_wrongs.size(); ++i) {
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out_wrong_t.device(place) += EigenTensor<int, 1>::From(*in_wrongs[i]);
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}
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auto in_corrects = ctx.MultiInput<Tensor>("InCorrects");
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for (size_t i = 0; i < in_corrects.size(); ++i) {
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out_correct_t.device(place) += EigenTensor<int, 1>::From(*in_corrects[i]);
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}
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// compute
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for (int64_t i = 0; i < predictions->numel(); ++i) {
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if (predictions_data[i] == labels_data[i]) {
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out_correct_data[predictions_data[i]] += 1;
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} else {
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out_wrong_data[labels_data[i]] += 1;
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out_wrong_data[predictions_data[i]] += 1;
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}
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}
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denominator_t = out_wrong_t + out_correct_t;
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valid_count_t =
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(denominator_t > denominator_t.constant(0.0f)).cast<int>().sum();
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for (int i = 0; i < num_classes; ++i) {
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if (denominator_data[i] == 0) {
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denominator_data[i] = 1;
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}
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}
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iou_sum_t =
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(out_correct_t.cast<float>() / denominator_t.cast<float>()).sum();
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out_mean_iou_data[0] += (iou_sum_data[0] / valid_count_data[0]);
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}
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};
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} // namespace operators
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} // namespace paddle
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