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308 lines
12 KiB
308 lines
12 KiB
/* Copyright (c) 2018 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 <vector>
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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, size_t 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, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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using Array2 = Eigen::DSizes<int64_t, 2>;
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using Array4 = Eigen::DSizes<int64_t, 4>;
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template <typename T>
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static inline bool isZero(T x) {
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return abs(x) < 1e-6;
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}
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template <typename T>
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static inline T sigmod(T x) {
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return 1.0 / (exp(-1.0 * x) + 1.0);
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}
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template <typename T>
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static inline T CalcMSEWithMask(const Tensor& x, const Tensor& y,
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const Tensor& mask) {
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auto x_t = EigenVector<T>::Flatten(x);
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auto y_t = EigenVector<T>::Flatten(y);
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auto mask_t = EigenVector<T>::Flatten(mask);
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T error_sum = 0.0;
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T points = 0.0;
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for (int i = 0; i < x_t.dimensions()[0]; i++) {
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if (mask_t(i)) {
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error_sum += pow(x_t(i) - y_t(i), 2);
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points += 1;
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}
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}
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return (error_sum / points);
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}
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template <typename T>
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static inline T CalcBCEWithMask(const Tensor& x, const Tensor& y,
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const Tensor& mask) {
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auto x_t = EigenVector<T>::Flatten(x);
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auto y_t = EigenVector<T>::Flatten(y);
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auto mask_t = EigenVector<T>::Flatten(mask);
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T error_sum = 0.0;
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T points = 0.0;
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for (int i = 0; i < x_t.dimensions()[0]; i++) {
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if (mask_t(i)) {
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error_sum +=
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-1.0 * (y_t(i) * log(x_t(i)) + (1.0 - y_t(i)) * log(1.0 - x_t(i)));
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points += 1;
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}
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}
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return (error_sum / points);
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}
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template <typename T>
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static void CalcPredResult(const Tensor& input, Tensor* pred_confs,
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Tensor* pred_classes, Tensor* pred_x, Tensor* pred_y,
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Tensor* pred_w, Tensor* pred_h,
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std::vector<int> anchors, const int class_num,
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const int stride) {
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const int n = input.dims()[0];
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const int c = input.dims()[1];
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const int h = input.dims()[2];
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const int w = input.dims()[3];
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const int anchor_num = anchors.size() / 2;
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const int box_attr_num = 5 + class_num;
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auto input_t = EigenTensor<T, 4>::From(input);
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// auto pred_boxes_t = EigenTensor<T, 5>::From(*pred_boxes);
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auto pred_confs_t = EigenTensor<T, 4>::From(*pred_confs);
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auto pred_classes_t = EigenTensor<T, 5>::From(*pred_classes);
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auto pred_x_t = EigenTensor<T, 4>::From(*pred_x);
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auto pred_y_t = EigenTensor<T, 4>::From(*pred_y);
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auto pred_w_t = EigenTensor<T, 4>::From(*pred_w);
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auto pred_h_t = EigenTensor<T, 4>::From(*pred_h);
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for (int i = 0; i < n; i++) {
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for (int an_idx = 0; an_idx < anchor_num; an_idx++) {
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float an_w = anchors[an_idx * 2] / stride;
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float an_h = anchors[an_idx * 2 + 1] / stride;
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for (int j = 0; j < h; j++) {
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for (int k = 0; k < w; k++) {
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pred_x_t(i, an_idx, j, k) =
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sigmod(input_t(i, box_attr_num * an_idx, j, k));
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pred_y_t(i, an_idx, j, k) =
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sigmod(input_t(i, box_attr_num * an_idx + 1, j, k));
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pred_w_t(i, an_idx, j, k) =
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input_t(i, box_attr_num * an_idx + 2, j, k);
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pred_h_t(i, an_idx, j, k) =
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input_t(i, box_attr_num * an_idx + 3, j, k);
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// pred_boxes_t(i, an_idx, j, k, 0) = pred_x_t(i, an_idx, j, k) + k;
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// pred_boxes_t(i, an_idx, j, k, 1) = pred_y_t(i, an_idx, j, k) + j;
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// pred_boxes_t(i, an_idx, j, k, 2) =
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// exp(pred_w_t(i, an_idx, j, k)) * an_w;
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// pred_boxes_t(i, an_idx, j, k, 3) =
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// exp(pred_h_t(i, an_idx, j, k)) * an_h;
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pred_confs_t(i, an_idx, j, k) =
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sigmod(input_t(i, box_attr_num * an_idx + 4, j, k));
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for (int c = 0; c < class_num; c++) {
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pred_classes_t(i, an_idx, j, k, c) =
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sigmod(input_t(i, box_attr_num * an_idx + 5 + c, j, k));
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}
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}
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}
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}
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}
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}
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template <typename T>
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static T CalcBoxIoU(std::vector<T> box1, std::vector<T> box2) {
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T b1_x1 = box1[0] - box1[2] / 2;
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T b1_x2 = box1[0] + box1[2] / 2;
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T b1_y1 = box1[1] - box1[3] / 2;
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T b1_y2 = box1[1] + box1[3] / 2;
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T b2_x1 = box2[0] - box2[2] / 2;
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T b2_x2 = box2[0] + box2[2] / 2;
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T b2_y1 = box2[1] - box2[3] / 2;
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T b2_y2 = box2[1] + box2[3] / 2;
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T b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1);
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T b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1);
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T inter_rect_x1 = std::max(b1_x1, b2_x1);
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T inter_rect_y1 = std::max(b1_y1, b2_y1);
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T inter_rect_x2 = std::min(b1_x2, b2_x2);
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T inter_rect_y2 = std::min(b1_y2, b2_y2);
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T inter_area = std::max(inter_rect_x2 - inter_rect_x1, static_cast<T>(0.0)) *
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std::max(inter_rect_y2 - inter_rect_y1, static_cast<T>(0.0));
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return inter_area / (b1_area + b2_area - inter_area);
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}
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template <typename T>
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static inline int GetPredLabel(const Tensor& pred_classes, int n,
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int best_an_index, int gj, int gi) {
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auto pred_classes_t = EigenTensor<T, 5>::From(pred_classes);
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T score = 0.0;
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int label = -1;
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for (int i = 0; i < pred_classes.dims()[4]; i++) {
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if (pred_classes_t(n, best_an_index, gj, gi, i) > score) {
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score = pred_classes_t(n, best_an_index, gj, gi, i);
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label = i;
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}
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}
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return label;
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}
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template <typename T>
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static void PrePorcessGTBox(const Tensor& gt_boxes, const float ignore_thresh,
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std::vector<int> anchors, const int img_height,
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const int grid_size, Tensor* obj_mask,
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Tensor* noobj_mask, Tensor* tx, Tensor* ty,
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Tensor* tw, Tensor* th, Tensor* tconf,
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Tensor* tclass) {
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const int n = gt_boxes.dims()[0];
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const int b = gt_boxes.dims()[1];
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const int anchor_num = anchors.size() / 2;
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auto gt_boxes_t = EigenTensor<T, 3>::From(gt_boxes);
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auto obj_mask_t = EigenTensor<int, 4>::From(*obj_mask).setConstant(0);
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auto noobj_mask_t = EigenTensor<int, 4>::From(*noobj_mask).setConstant(1);
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auto tx_t = EigenTensor<T, 4>::From(*tx).setConstant(0.0);
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auto ty_t = EigenTensor<T, 4>::From(*ty).setConstant(0.0);
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auto tw_t = EigenTensor<T, 4>::From(*tw).setConstant(0.0);
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auto th_t = EigenTensor<T, 4>::From(*th).setConstant(0.0);
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auto tconf_t = EigenTensor<T, 4>::From(*tconf).setConstant(0.0);
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auto tclass_t = EigenTensor<T, 5>::From(*tclass).setConstant(0.0);
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for (int i = 0; i < n; i++) {
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for (int j = 0; j < b; j++) {
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if (isZero(gt_boxes_t(i, j, 0)) && isZero(gt_boxes_t(i, j, 1)) &&
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isZero(gt_boxes_t(i, j, 2)) && isZero(gt_boxes_t(i, j, 3))) {
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continue;
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}
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int gt_label = gt_boxes_t(i, j, 0);
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T gx = gt_boxes_t(i, j, 1) * grid_size;
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T gy = gt_boxes_t(i, j, 2) * grid_size;
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T gw = gt_boxes_t(i, j, 3) * grid_size;
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T gh = gt_boxes_t(i, j, 4) * grid_size;
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int gi = static_cast<int>(gx);
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int gj = static_cast<int>(gy);
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T max_iou = static_cast<T>(-1);
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T iou;
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int best_an_index = -1;
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std::vector<T> gt_box({0, 0, gw, gh});
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for (int an_idx = 0; an_idx < anchor_num; an_idx++) {
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std::vector<T> anchor_shape({0, 0, static_cast<T>(anchors[2 * an_idx]),
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static_cast<T>(anchors[2 * an_idx + 1])});
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iou = CalcBoxIoU<T>(gt_box, anchor_shape);
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if (iou > max_iou) {
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max_iou = iou;
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best_an_index = an_idx;
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}
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if (iou > ignore_thresh) {
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noobj_mask_t(b, an_idx, gj, gi) = 0;
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}
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}
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obj_mask_t(b, best_an_index, gj, gi) = 1;
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noobj_mask_t(b, best_an_index, gj, gi) = 1;
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tx_t(i, best_an_index, gj, gi) = gx - gi;
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ty_t(i, best_an_index, gj, gi) = gy - gj;
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tw_t(i, best_an_index, gj, gi) = log(gw / anchors[2 * best_an_index]);
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th_t(i, best_an_index, gj, gi) = log(gh / anchors[2 * best_an_index + 1]);
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tclass_t(b, best_an_index, gj, gi, gt_label) = 1;
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tconf_t(b, best_an_index, gj, gi) = 1;
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}
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}
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noobj_mask_t = noobj_mask_t - obj_mask_t;
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}
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template <typename DeviceContext, typename T>
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class Yolov3LossKernel : 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* input = ctx.Input<Tensor>("X");
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auto* gt_boxes = ctx.Input<Tensor>("GTBox");
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auto* loss = ctx.Output<Tensor>("Loss");
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int img_height = ctx.Attr<int>("img_height");
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auto anchors = ctx.Attr<std::vector<int>>("anchors");
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int class_num = ctx.Attr<int>("class_num");
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float ignore_thresh = ctx.Attr<float>("ignore_thresh");
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const int n = input->dims()[0];
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const int c = input->dims()[1];
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const int h = input->dims()[2];
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const int w = input->dims()[3];
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const int an_num = anchors.size() / 2;
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const T stride = static_cast<T>(img_height) / h;
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Tensor pred_x, pred_y, pred_w, pred_h;
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Tensor pred_confs, pred_classes;
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pred_x.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
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pred_y.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
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pred_w.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
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pred_h.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
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pred_confs.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
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pred_classes.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
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CalcPredResult<T>(*input, &pred_confs, &pred_classes, &pred_x, &pred_y,
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&pred_w, &pred_h, anchors, class_num, stride);
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Tensor obj_mask, noobj_mask;
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Tensor tx, ty, tw, th, tconf, tclass;
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obj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
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noobj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
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tx.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
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ty.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
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tw.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
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th.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
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tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
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tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
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PrePorcessGTBox<T>(*gt_boxes, ignore_thresh, anchors, img_height, h,
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&obj_mask, &noobj_mask, &tx, &ty, &tw, &th, &tconf,
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&tclass);
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T loss_x = CalcMSEWithMask<T>(pred_x, tx, obj_mask);
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T loss_y = CalcMSEWithMask<T>(pred_y, ty, obj_mask);
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T loss_w = CalcMSEWithMask<T>(pred_w, tw, obj_mask);
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T loss_h = CalcMSEWithMask<T>(pred_h, th, obj_mask);
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T loss_conf_true = CalcBCEWithMask<T>(pred_confs, tconf, obj_mask);
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T loss_conf_false = CalcBCEWithMask<T>(pred_confs, tconf, noobj_mask);
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T loss_class = CalcBCEWithMask<T>(pred_classes, tclass, obj_mask);
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auto* loss_data = loss->mutable_data<T>({1}, ctx.GetPlace());
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loss_data[0] = loss_x + loss_y + loss_w + loss_h + loss_conf_true +
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loss_conf_false + loss_class;
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}
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};
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template <typename DeviceContext, typename T>
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class Yolov3LossGradKernel : 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* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
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}
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};
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} // namespace operators
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} // namespace paddle
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