/*
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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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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*/

#include <algorithm>
#include <cmath>
#include <vector>

#include "caffe/layers/infogain_loss_layer.hpp"
#include "caffe/util/io.hpp"  // for blob reading of matrix H
#include "caffe/util/math_functions.hpp"

namespace caffe {

template <typename Dtype>
void InfogainLossLayer<Dtype>::LayerSetUp(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  LossLayer<Dtype>::LayerSetUp(bottom, top);
  // internal softmax layer
  LayerParameter softmax_layer_param(this->layer_param_);
  SoftmaxParameter* softmax_param = softmax_layer_param.mutable_softmax_param();
  softmax_param->set_axis(this->layer_param_.infogain_loss_param().axis());
  softmax_layer_param.set_type("Softmax");
  softmax_layer_param.clear_loss_weight();
  softmax_layer_param.add_loss_weight(1);
  softmax_layer_ = LayerRegistry<Dtype>::CreateLayer(softmax_layer_param);
  softmax_bottom_vec_.clear();
  softmax_bottom_vec_.push_back(bottom[0]);
  softmax_top_vec_.clear();
  softmax_top_vec_.push_back(&prob_);
  softmax_layer_->SetUp(softmax_bottom_vec_, softmax_top_vec_);

  // ignore label
  has_ignore_label_ =
    this->layer_param_.loss_param().has_ignore_label();
  if (has_ignore_label_) {
    ignore_label_ = this->layer_param_.loss_param().ignore_label();
  }
  // normalization
  CHECK(!this->layer_param_.loss_param().has_normalize())
    << "normalize is deprecated. use \"normalization\"";
  normalization_ = this->layer_param_.loss_param().normalization();
  // matrix H
  if (bottom.size() < 3) {
    CHECK(this->layer_param_.infogain_loss_param().has_source())
        << "Infogain matrix source must be specified.";
    BlobProto blob_proto;
    ReadProtoFromBinaryFile(
      this->layer_param_.infogain_loss_param().source(), &blob_proto);
    infogain_.FromProto(blob_proto);
  }
}

template <typename Dtype>
void InfogainLossLayer<Dtype>::Reshape(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  LossLayer<Dtype>::Reshape(bottom, top);
  softmax_layer_->Reshape(softmax_bottom_vec_, softmax_top_vec_);
  infogain_axis_ =
    bottom[0]->CanonicalAxisIndex(
      this->layer_param_.infogain_loss_param().axis());
  outer_num_ = bottom[0]->count(0, infogain_axis_);
  inner_num_ = bottom[0]->count(infogain_axis_ + 1);
  CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
      << "Number of labels must match number of predictions; "
      << "e.g., if infogain axis == 1 and prediction shape is (N, C, H, W), "
      << "label count (number of labels) must be N*H*W, "
      << "with integer values in {0, 1, ..., C-1}.";
  num_labels_ = bottom[0]->shape(infogain_axis_);
  Blob<Dtype>* infogain = NULL;
  if (bottom.size() < 3) {
    infogain = &infogain_;
  } else {
    infogain = bottom[2];
  }
  CHECK_EQ(infogain->count(), num_labels_*num_labels_);
  sum_rows_H_.Reshape(vector<int>(1, num_labels_));
  if (bottom.size() == 2) {
    // H is provided as a parameter and will not change. sum rows once
    sum_rows_of_H(infogain);
  }
  if (top.size() >= 2) {
    // softmax output
    top[1]->ReshapeLike(*bottom[0]);
  }
}

template <typename Dtype>
Dtype InfogainLossLayer<Dtype>::get_normalizer(
    LossParameter_NormalizationMode normalization_mode, int valid_count) {
  Dtype normalizer;
  switch (normalization_mode) {
    case LossParameter_NormalizationMode_FULL:
      normalizer = Dtype(outer_num_ * inner_num_);
      break;
    case LossParameter_NormalizationMode_VALID:
      if (valid_count == -1) {
        normalizer = Dtype(outer_num_ * inner_num_);
      } else {
        normalizer = Dtype(valid_count);
      }
      break;
    case LossParameter_NormalizationMode_BATCH_SIZE:
      normalizer = Dtype(outer_num_);
      break;
    case LossParameter_NormalizationMode_PRE_FIXED:
      normalizer = Dtype(this->layer_param_.loss_param().pre_fixed_normalizer());
      break;
    case LossParameter_NormalizationMode_NONE:
      normalizer = Dtype(1);
      break;
    default:
      LOG(FATAL) << "Unknown normalization mode: "
          << LossParameter_NormalizationMode_Name(normalization_mode);
  }
#ifdef USE_MLSL
  if (normalization_mode != LossParameter_NormalizationMode_NONE) {
    Dtype allnodes_normalizer(normalizer);
    DLOG(INFO) << "node id: " << mn::get_node_id() << ", normalizer: " << normalizer;
    if (has_ignore_label_) {
      mn::allreduce(&allnodes_normalizer, 1);
    } else {
      // We assume local bs is same across all nodes
      allnodes_normalizer *= mn::get_group_size();
    }
    normalizer = allnodes_normalizer;
  }
#endif
  DLOG(INFO) << "Final normalizer: " << normalizer;
  // Some users will have no labels for some examples in order to 'turn off' a
  // particular loss in a multi-task setup. The max prevents NaNs in that case.
  this->cached_normalizer_ = std::max(Dtype(1.0), normalizer);
  return this->cached_normalizer_;
}

template <typename Dtype>
void InfogainLossLayer<Dtype>::sum_rows_of_H(const Blob<Dtype>* H) {
  CHECK_EQ(H->count(), num_labels_*num_labels_)
    << "H must be " << num_labels_ << "x" << num_labels_;
  const Dtype* infogain_mat = H->cpu_data();
  Dtype* sum = sum_rows_H_.mutable_cpu_data();
  for ( int row = 0; row < num_labels_ ; row++ ) {
    sum[row] = 0;
    for ( int col = 0; col < num_labels_ ; col++ ) {
      sum[row] += infogain_mat[row*num_labels_+col];
    }
  }
}

template <typename Dtype>
void InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) {
  // The forward pass computes the softmax prob values.
  softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
  const Dtype* prob_data = prob_.cpu_data();
  const Dtype* bottom_label = bottom[1]->cpu_data();
  const Dtype* infogain_mat = NULL;
  if (bottom.size() < 3) {
    infogain_mat = infogain_.cpu_data();
  } else {
    infogain_mat = bottom[2]->cpu_data();
  }
  int count = 0;
  Dtype loss = 0;
  for (int i = 0; i < outer_num_; ++i) {
    for (int j = 0; j < inner_num_; j++) {
      const int label_value =
        static_cast<int>(bottom_label[i * inner_num_ + j]);
      if (has_ignore_label_ && label_value == ignore_label_) {
        continue;
      }
      DCHECK_GE(label_value, 0);
      DCHECK_LT(label_value, num_labels_);
      for (int l = 0; l < num_labels_; l++) {
        loss -= infogain_mat[label_value * num_labels_ + l] *
          log(std::max(
                prob_data[i * inner_num_*num_labels_ + l * inner_num_ + j],
                Dtype(kLOG_THRESHOLD)));
      }
      ++count;
    }
  }
  top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_, count);
  if (top.size() == 2) {
    top[1]->ShareData(prob_);
  }
}

template <typename Dtype>
void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down,
    const vector<Blob<Dtype>*>& bottom) {
  if (propagate_down[1]) {
    LOG(FATAL) << this->type()
               << " Layer cannot backpropagate to label inputs.";
  }
  if (propagate_down.size() > 2 && propagate_down[2]) {
    LOG(FATAL) << this->type()
               << " Layer cannot backpropagate to infogain inputs.";
  }
  if (propagate_down[0]) {
    const Dtype* prob_data = prob_.cpu_data();
    const Dtype* bottom_label = bottom[1]->cpu_data();
    const Dtype* infogain_mat = NULL;
    if (bottom.size() < 3) {
      infogain_mat = infogain_.cpu_data();
    } else {
      infogain_mat = bottom[2]->cpu_data();
      // H is provided as a "bottom" and might change. sum rows every time.
      sum_rows_of_H(bottom[2]);
    }
    const Dtype* sum_rows_H = sum_rows_H_.cpu_data();
    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
    const int dim = bottom[0]->count() / outer_num_;
    int count = 0;
    for (int i = 0; i < outer_num_; ++i) {
      for (int j = 0; j < inner_num_; ++j) {
        const int label_value =
          static_cast<int>(bottom_label[i * inner_num_ + j]);
        DCHECK_GE(label_value, 0);
        DCHECK_LT(label_value, num_labels_);
        if (has_ignore_label_ && label_value == ignore_label_) {
          for (int l = 0; l < num_labels_; ++l) {
            bottom_diff[i * dim + l * inner_num_ + j] = 0;
          }
        } else {
          for (int l = 0; l < num_labels_; ++l) {
            bottom_diff[i * dim + l * inner_num_ + j] =
               prob_data[i*dim + l*inner_num_ + j]*sum_rows_H[label_value]
               - infogain_mat[label_value * num_labels_ + l];
          }
          ++count;
        }
      }
    }
    // Scale gradient
    Dtype loss_weight = top[0]->cpu_diff()[0] / this->cached_normalizer_;
    caffe_scal(bottom[0]->count(), loss_weight, bottom_diff);
  }
}

INSTANTIATE_CLASS(InfogainLossLayer);
REGISTER_LAYER_CLASS(InfogainLoss);
}  // namespace caffe
