/*
All modification made by Intel Corporation: © 2016 Intel Corporation

All contributions by the University of California:
Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
All rights reserved.

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Copyright (c) 2014, 2015, the respective contributors
All rights reserved.
For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md


Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

    * Redistributions of source code must retain the above copyright notice,
      this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of Intel Corporation nor the names of its contributors
      may be used to endorse or promote products derived from this software
      without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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*/

#include <algorithm>
#include <map>
#include <utility>
#include <vector>

#include "caffe/layers/multibox_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/util/performance.hpp"

namespace caffe {

template <typename Dtype>
void MultiBoxLossLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
  LossLayer<Dtype>::LayerSetUp(bottom, top);
  if (this->layer_param_.propagate_down_size() == 0) {
    this->layer_param_.add_propagate_down(true);
    this->layer_param_.add_propagate_down(true);
    this->layer_param_.add_propagate_down(false);
    this->layer_param_.add_propagate_down(false);
  }
  const MultiBoxLossParameter& multibox_loss_param =
      this->layer_param_.multibox_loss_param();
  multibox_loss_param_ = this->layer_param_.multibox_loss_param();

  num_ = bottom[0]->num();
  num_priors_ = bottom[2]->height() / 4;
  // Get other parameters.
  CHECK(multibox_loss_param.has_num_classes()) << "Must provide num_classes.";
  num_classes_ = multibox_loss_param.num_classes();
  CHECK_GE(num_classes_, 1) << "num_classes should not be less than 1.";
  share_location_ = multibox_loss_param.share_location();
  loc_classes_ = share_location_ ? 1 : num_classes_;
  background_label_id_ = multibox_loss_param.background_label_id();
  use_difficult_gt_ = multibox_loss_param.use_difficult_gt();
  mining_type_ = multibox_loss_param.mining_type();
  if (multibox_loss_param.has_do_neg_mining()) {
    LOG(WARNING) << "do_neg_mining is deprecated, use mining_type instead.";
    do_neg_mining_ = multibox_loss_param.do_neg_mining();
    CHECK_EQ(do_neg_mining_,
             mining_type_ != MultiBoxLossParameter_MiningType_NONE);
  }
  do_neg_mining_ = mining_type_ != MultiBoxLossParameter_MiningType_NONE;

  if (!this->layer_param_.loss_param().has_normalization() &&
      this->layer_param_.loss_param().has_normalize()) {
    normalization_ = this->layer_param_.loss_param().normalize() ?
                     LossParameter_NormalizationMode_VALID :
                     LossParameter_NormalizationMode_BATCH_SIZE;
  } else {
    normalization_ = this->layer_param_.loss_param().normalization();
  }

  if (do_neg_mining_) {
    CHECK(share_location_)
        << "Currently only support negative mining if share_location is true.";
  }

  vector<int> loss_shape(1, 1);
  // Set up localization loss layer.
  loc_weight_ = multibox_loss_param.loc_weight();
  loc_loss_type_ = multibox_loss_param.loc_loss_type();
  // fake shape.
  vector<int> loc_shape(1, 1);
  loc_shape.push_back(4);
  loc_pred_.Reshape(loc_shape);
  loc_gt_.Reshape(loc_shape);
  loc_bottom_vec_.push_back(&loc_pred_);
  loc_bottom_vec_.push_back(&loc_gt_);
  loc_loss_.Reshape(loss_shape);
  loc_top_vec_.push_back(&loc_loss_);
  if (loc_loss_type_ == MultiBoxLossParameter_LocLossType_L2) {
    LayerParameter layer_param;
    layer_param.set_name(this->layer_param_.name() + "_l2_loc");
    layer_param.set_type("EuclideanLoss");
    layer_param.add_loss_weight(loc_weight_);
    layer_param.mutable_loss_param()->set_normalization(
        LossParameter_NormalizationMode_NONE);
    loc_loss_layer_ = LayerRegistry<Dtype>::CreateLayer(layer_param);
    loc_loss_layer_->SetUp(loc_bottom_vec_, loc_top_vec_);
  } else if (loc_loss_type_ == MultiBoxLossParameter_LocLossType_SMOOTH_L1) {
    LayerParameter layer_param;
    layer_param.set_name(this->layer_param_.name() + "_smooth_L1_loc");
    layer_param.set_type("SmoothL1Loss");
    layer_param.add_loss_weight(loc_weight_);
    layer_param.mutable_loss_param()->set_normalization(
        LossParameter_NormalizationMode_NONE);
    loc_loss_layer_ = LayerRegistry<Dtype>::CreateLayer(layer_param);
    loc_loss_layer_->SetUp(loc_bottom_vec_, loc_top_vec_);
  } else {
    LOG(FATAL) << "Unknown localization loss type.";
  }
  // Set up confidence loss layer.
  conf_loss_type_ = multibox_loss_param.conf_loss_type();
  conf_bottom_vec_.push_back(&conf_pred_);
  conf_bottom_vec_.push_back(&conf_gt_);
  conf_loss_.Reshape(loss_shape);
  conf_top_vec_.push_back(&conf_loss_);
  if (conf_loss_type_ == MultiBoxLossParameter_ConfLossType_SOFTMAX) {
    CHECK_GE(background_label_id_, 0)
        << "background_label_id should be within [0, num_classes) for Softmax.";
    CHECK_LT(background_label_id_, num_classes_)
        << "background_label_id should be within [0, num_classes) for Softmax.";
    LayerParameter layer_param;
    layer_param.set_name(this->layer_param_.name() + "_softmax_conf");
    layer_param.set_type("SoftmaxWithLoss");
    layer_param.add_loss_weight(Dtype(1.));
    layer_param.mutable_loss_param()->set_normalization(
        LossParameter_NormalizationMode_NONE);
    SoftmaxParameter* softmax_param = layer_param.mutable_softmax_param();
    softmax_param->set_axis(1);
    // Fake reshape.
    vector<int> conf_shape(1, 1);
    conf_gt_.Reshape(conf_shape);
    conf_shape.push_back(num_classes_);
    conf_pred_.Reshape(conf_shape);
    conf_loss_layer_ = LayerRegistry<Dtype>::CreateLayer(layer_param);
    conf_loss_layer_->SetUp(conf_bottom_vec_, conf_top_vec_);
  } else if (conf_loss_type_ == MultiBoxLossParameter_ConfLossType_LOGISTIC) {
    LayerParameter layer_param;
    layer_param.set_name(this->layer_param_.name() + "_logistic_conf");
    layer_param.set_type("SigmoidCrossEntropyLoss");
    layer_param.add_loss_weight(Dtype(1.));
    layer_param.mutable_loss_param()->set_normalization(
        LossParameter_NormalizationMode_NONE);
    // Fake reshape.
    vector<int> conf_shape(1, 1);
    conf_shape.push_back(num_classes_);
    conf_gt_.Reshape(conf_shape);
    conf_pred_.Reshape(conf_shape);
    conf_loss_layer_ = LayerRegistry<Dtype>::CreateLayer(layer_param);
    conf_loss_layer_->SetUp(conf_bottom_vec_, conf_top_vec_);
  } else {
    LOG(FATAL) << "Unknown confidence loss type.";
  }
}

template <typename Dtype>
void MultiBoxLossLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
  LossLayer<Dtype>::Reshape(bottom, top);
  num_ = bottom[0]->num();
  num_priors_ = bottom[2]->height() / 4;
  num_gt_ = bottom[3]->height();
  CHECK_EQ(bottom[0]->num(), bottom[1]->num());
  CHECK_EQ(num_priors_ * loc_classes_ * 4, bottom[0]->channels())
      << "Number of priors must match number of location predictions.";
  CHECK_EQ(num_priors_ * num_classes_, bottom[1]->channels())
      << "Number of priors must match number of confidence predictions.";
}

template <typename Dtype>
void MultiBoxLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) {
  const Dtype* loc_data = bottom[0]->cpu_data();
  const Dtype* conf_data = bottom[1]->cpu_data();
  const Dtype* prior_data = bottom[2]->cpu_data();
  const Dtype* gt_data = bottom[3]->cpu_data();

  // Retrieve all ground truth.
  map<int, vector<NormalizedBBox> > all_gt_bboxes;
  GetGroundTruth(gt_data, num_gt_, background_label_id_, use_difficult_gt_,
                 &all_gt_bboxes);

  // Retrieve all prior bboxes. It is same within a batch since we assume all
  // images in a batch are of same dimension.
  vector<NormalizedBBox> prior_bboxes(num_priors_);
  vector<vector<float> > prior_variances(num_priors_);
  GetPriorBBoxes(prior_data, num_priors_, &prior_bboxes, &prior_variances);

  // Retrieve all predictions.
  vector<LabelBBox> all_loc_preds;
  GetLocPredictions(loc_data, num_, num_priors_, loc_classes_, share_location_,
                    &all_loc_preds);

  // Find matches between source bboxes and ground truth bboxes.
  vector<map<int, vector<float> > > all_match_overlaps;
  FindMatches(all_loc_preds, all_gt_bboxes, prior_bboxes, prior_variances,
              multibox_loss_param_, &all_match_overlaps, &all_match_indices_);

  num_matches_ = 0;
  int num_negs = 0;
  // Sample hard negative (and positive) examples based on mining type.
  MineHardExamples(*bottom[1], all_loc_preds, all_gt_bboxes, prior_bboxes,
                   prior_variances, all_match_overlaps, multibox_loss_param_,
                   &num_matches_, &num_negs, &all_match_indices_,
                   &all_neg_indices_);

  if (num_matches_ >= 1) {
    // Form data to pass on to loc_loss_layer_.
    vector<int> loc_shape(2);
    loc_shape[0] = 1;
    loc_shape[1] = num_matches_ * 4;
    loc_pred_.Reshape(loc_shape);
    loc_gt_.Reshape(loc_shape);
    Dtype* loc_pred_data = loc_pred_.mutable_cpu_data();
    Dtype* loc_gt_data = loc_gt_.mutable_cpu_data();
    EncodeLocPrediction(all_loc_preds, all_gt_bboxes, all_match_indices_,
                        prior_bboxes, prior_variances, multibox_loss_param_,
                        loc_pred_data, loc_gt_data);
    {PERFORMANCE_MEASUREMENT_BEGIN();
    loc_loss_layer_->Reshape(loc_bottom_vec_, loc_top_vec_);
    loc_loss_layer_->Forward(loc_bottom_vec_, loc_top_vec_);
    PERFORMANCE_MEASUREMENT_END_STATIC("FW_Smooth_L1");}
  } else {
    loc_loss_.mutable_cpu_data()[0] = 0;
  }

  // Form data to pass on to conf_loss_layer_.
  if (do_neg_mining_) {
    num_conf_ = num_matches_ + num_negs;
  } else {
    num_conf_ = num_ * num_priors_;
  }
  if (num_conf_ >= 1) {
    // Reshape the confidence data.
    vector<int> conf_shape;
    if (conf_loss_type_ == MultiBoxLossParameter_ConfLossType_SOFTMAX) {
      conf_shape.push_back(num_conf_);
      conf_gt_.Reshape(conf_shape);
      conf_shape.push_back(num_classes_);
      conf_pred_.Reshape(conf_shape);
    } else if (conf_loss_type_ == MultiBoxLossParameter_ConfLossType_LOGISTIC) {
      conf_shape.push_back(1);
      conf_shape.push_back(num_conf_);
      conf_shape.push_back(num_classes_);
      conf_gt_.Reshape(conf_shape);
      conf_pred_.Reshape(conf_shape);
    } else {
      LOG(FATAL) << "Unknown confidence loss type.";
    }
    if (!do_neg_mining_) {
      // Consider all scores.
      // Share data and diff with bottom[1].
      CHECK_EQ(conf_pred_.count(), bottom[1]->count());
      conf_pred_.ShareData(*(bottom[1]));
    }
    Dtype* conf_pred_data = conf_pred_.mutable_cpu_data();
    Dtype* conf_gt_data = conf_gt_.mutable_cpu_data();
    caffe_set(conf_gt_.count(), Dtype(background_label_id_), conf_gt_data);
    EncodeConfPrediction(conf_data, num_, num_priors_, multibox_loss_param_,
                         all_match_indices_, all_neg_indices_, all_gt_bboxes,
                         conf_pred_data, conf_gt_data);
    {PERFORMANCE_MEASUREMENT_BEGIN();
    conf_loss_layer_->Reshape(conf_bottom_vec_, conf_top_vec_);
    conf_loss_layer_->Forward(conf_bottom_vec_, conf_top_vec_);
    PERFORMANCE_MEASUREMENT_END_STATIC("FW_Softmax");}
  } else {
    conf_loss_.mutable_cpu_data()[0] = 0;
  }

  top[0]->mutable_cpu_data()[0] = 0;
  // To make sure we don't use the cached normalizer value
  Dtype normalizer = LossLayer<Dtype>::GetNormalizer(
      normalization_, num_, num_priors_, num_matches_);
  if (this->layer_param_.propagate_down(0)) {
    top[0]->mutable_cpu_data()[0] +=
        loc_weight_ * loc_loss_.cpu_data()[0] / normalizer;
  }
  if (this->layer_param_.propagate_down(1)) {
    top[0]->mutable_cpu_data()[0] += conf_loss_.cpu_data()[0] / normalizer;
  }
}

template <typename Dtype>
void MultiBoxLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down,
    const vector<Blob<Dtype>*>& bottom) {

  if (propagate_down[2]) {
    LOG(FATAL) << this->type()
        << " Layer cannot backpropagate to prior inputs.";
  }
  if (propagate_down[3]) {
    LOG(FATAL) << this->type()
        << " Layer cannot backpropagate to label inputs.";
  }

  // Back propagate on location prediction.
  if (propagate_down[0]) {
    Dtype* loc_bottom_diff = bottom[0]->mutable_cpu_diff();
    caffe_set(bottom[0]->count(), Dtype(0), loc_bottom_diff);
    if (num_matches_ >= 1) {
      vector<bool> loc_propagate_down;
      // Only back propagate on prediction, not ground truth.
      loc_propagate_down.push_back(true);
      loc_propagate_down.push_back(false);
      {PERFORMANCE_MEASUREMENT_BEGIN();
      loc_loss_layer_->Backward(loc_top_vec_, loc_propagate_down,
                                loc_bottom_vec_);
      PERFORMANCE_MEASUREMENT_END_STATIC("BW_Smooth_L1");}
      // Scale gradient.
      Dtype loss_weight = top[0]->cpu_diff()[0] / this->cached_normalizer_;
      caffe_scal(loc_pred_.count(), loss_weight, loc_pred_.mutable_cpu_diff());
      // Copy gradient back to bottom[0].
      const Dtype* loc_pred_diff = loc_pred_.cpu_diff();
      int count = 0;
      for (int i = 0; i < num_; ++i) {
        for (map<int, vector<int> >::iterator it =
             all_match_indices_[i].begin();
             it != all_match_indices_[i].end(); ++it) {
          const int label = share_location_ ? 0 : it->first;
          const vector<int>& match_index = it->second;
          for (int j = 0; j < match_index.size(); ++j) {
            if (match_index[j] <= -1) {
              continue;
            }
            // Copy the diff to the right place.
            int start_idx = loc_classes_ * 4 * j + label * 4;
            caffe_copy<Dtype>(4, loc_pred_diff + count * 4,
                              loc_bottom_diff + start_idx);
            ++count;
          }
        }
        loc_bottom_diff += bottom[0]->offset(1);
      }
    }
  }

  // Back propagate on confidence prediction.
  if (propagate_down[1]) {
    Dtype* conf_bottom_diff = bottom[1]->mutable_cpu_diff();
    caffe_set(bottom[1]->count(), Dtype(0), conf_bottom_diff);
    if (num_conf_ >= 1) {
      vector<bool> conf_propagate_down;
      // Only back propagate on prediction, not ground truth.
      conf_propagate_down.push_back(true);
      conf_propagate_down.push_back(false);
      {PERFORMANCE_MEASUREMENT_BEGIN();
      conf_loss_layer_->Backward(conf_top_vec_, conf_propagate_down,
                                 conf_bottom_vec_);
      PERFORMANCE_MEASUREMENT_END_STATIC("BW_Softmax");}
      // Scale gradient.
      Dtype loss_weight = top[0]->cpu_diff()[0] / this->cached_normalizer_;
      caffe_scal(conf_pred_.count(), loss_weight,
                 conf_pred_.mutable_cpu_diff());
      // Copy gradient back to bottom[1].
      const Dtype* conf_pred_diff = conf_pred_.cpu_diff();
      if (do_neg_mining_) {
        int count = 0;
        for (int i = 0; i < num_; ++i) {
          // Copy matched (positive) bboxes scores' diff.
          const map<int, vector<int> >& match_indices = all_match_indices_[i];
          for (map<int, vector<int> >::const_iterator it =
               match_indices.begin(); it != match_indices.end(); ++it) {
            const vector<int>& match_index = it->second;
            CHECK_EQ(match_index.size(), num_priors_);
            for (int j = 0; j < num_priors_; ++j) {
              if (match_index[j] <= -1) {
                continue;
              }
              // Copy the diff to the right place.
              caffe_copy<Dtype>(num_classes_,
                                conf_pred_diff + count * num_classes_,
                                conf_bottom_diff + j * num_classes_);
              ++count;
            }
          }
          // Copy negative bboxes scores' diff.
          for (int n = 0; n < all_neg_indices_[i].size(); ++n) {
            int j = all_neg_indices_[i][n];
            CHECK_LT(j, num_priors_);
            caffe_copy<Dtype>(num_classes_,
                              conf_pred_diff + count * num_classes_,
                              conf_bottom_diff + j * num_classes_);
            ++count;
          }
          conf_bottom_diff += bottom[1]->offset(1);
        }
      } else {
        // The diff is already computed and stored.
        bottom[1]->ShareDiff(conf_pred_);
      }
    }
  }

  // After backward, remove match statistics.
  all_match_indices_.clear();
  all_neg_indices_.clear();
}

INSTANTIATE_CLASS(MultiBoxLossLayer);
REGISTER_LAYER_CLASS(MultiBoxLoss);

}  // namespace caffe
