/*
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.

All other contributions:
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
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/

#include <string>
#include <vector>

#include "boost/algorithm/string.hpp"
#include "google/protobuf/text_format.h"

#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/net.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/db.hpp"
#include "caffe/util/format.hpp"
#include "caffe/util/io.hpp"

using caffe::Blob;
using caffe::Caffe;
using caffe::Datum;
using caffe::Net;
using std::string;
namespace db = caffe::db;

template<typename Dtype>
int feature_extraction_pipeline(int argc, char** argv);

int main(int argc, char** argv) {
#ifdef USE_MLSL
  caffe::mn::init(&argc, &argv);
#endif
  return feature_extraction_pipeline<float>(argc, argv);
//  return feature_extraction_pipeline<double>(argc, argv);
}

template<typename Dtype>
int feature_extraction_pipeline(int argc, char** argv) {
  ::google::InitGoogleLogging(argv[0]);
  const int num_required_args = 7;
  if (argc < num_required_args) {
    LOG(ERROR)<<
    "This program takes in a trained network and an input data layer, and then"
    " extract features of the input data produced by the net.\n"
    "Usage: extract_features  pretrained_net_param"
    "  feature_extraction_proto_file  extract_feature_blob_name1[,name2,...]"
    "  save_feature_dataset_name1[,name2,...]  num_mini_batches  db_type"
    "  [CPU/GPU] [DEVICE_ID=0]\n"
    "Note: you can extract multiple features in one pass by specifying"
    " multiple feature blob names and dataset names separated by ','."
    " The names cannot contain white space characters and the number of blobs"
    " and datasets must be equal.";
    return 1;
  }
  int arg_pos = num_required_args;

  arg_pos = num_required_args;
  if (argc > arg_pos && strcmp(argv[arg_pos], "GPU") == 0) {
    LOG(ERROR)<< "Using GPU";
    int device_id = 0;
    if (argc > arg_pos + 1) {
      device_id = atoi(argv[arg_pos + 1]);
      CHECK_GE(device_id, 0);
    }
    LOG(ERROR) << "Using Device_id=" << device_id;
    Caffe::SetDevice(device_id);
    Caffe::set_mode(Caffe::GPU);
  } else {
    LOG(ERROR) << "Using CPU";
    Caffe::set_mode(Caffe::CPU);
  }

  arg_pos = 0;  // the name of the executable
  std::string pretrained_binary_proto(argv[++arg_pos]);

  // Expected prototxt contains at least one data layer such as
  //  the layer data_layer_name and one feature blob such as the
  //  fc7 top blob to extract features.
  /*
   layers {
     name: "data_layer_name"
     type: DATA
     data_param {
       source: "/path/to/your/images/to/extract/feature/images_leveldb"
       mean_file: "/path/to/your/image_mean.binaryproto"
       batch_size: 128
       crop_size: 227
       mirror: false
     }
     top: "data_blob_name"
     top: "label_blob_name"
   }
   layers {
     name: "drop7"
     type: DROPOUT
     dropout_param {
       dropout_ratio: 0.5
     }
     bottom: "fc7"
     top: "fc7"
   }
   */
  std::string feature_extraction_proto(argv[++arg_pos]);
  boost::shared_ptr<Net<Dtype> > feature_extraction_net(
      new Net<Dtype>(feature_extraction_proto, caffe::TEST));
  feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);

  std::string extract_feature_blob_names(argv[++arg_pos]);
  std::vector<std::string> blob_names;
  boost::split(blob_names, extract_feature_blob_names, boost::is_any_of(","));

  std::string save_feature_dataset_names(argv[++arg_pos]);
  std::vector<std::string> dataset_names;
  boost::split(dataset_names, save_feature_dataset_names,
               boost::is_any_of(","));
  CHECK_EQ(blob_names.size(), dataset_names.size()) <<
      " the number of blob names and dataset names must be equal";
  size_t num_features = blob_names.size();

  for (size_t i = 0; i < num_features; i++) {
    CHECK(feature_extraction_net->has_blob(blob_names[i]))
        << "Unknown feature blob name " << blob_names[i]
        << " in the network " << feature_extraction_proto;
  }

  int num_mini_batches = atoi(argv[++arg_pos]);

  std::vector<boost::shared_ptr<db::DB> > feature_dbs;
  std::vector<boost::shared_ptr<db::Transaction> > txns;
  const char* db_type = argv[++arg_pos];
  for (size_t i = 0; i < num_features; ++i) {
    LOG(INFO)<< "Opening dataset " << dataset_names[i];
    boost::shared_ptr<db::DB> db(db::GetDB(db_type));
    db->Open(dataset_names.at(i), db::NEW);
    feature_dbs.push_back(db);
    boost::shared_ptr<db::Transaction> txn(db->NewTransaction());
    txns.push_back(txn);
  }

  LOG(ERROR)<< "Extracting Features";

  Datum datum;
  std::vector<int> image_indices(num_features, 0);
  for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
    feature_extraction_net->Forward();
    for (int i = 0; i < num_features; ++i) {
      const boost::shared_ptr<Blob<Dtype> > feature_blob =
        feature_extraction_net->blob_by_name(blob_names[i]);
      int batch_size = feature_blob->num();
      int dim_features = feature_blob->count() / batch_size;
      const Dtype* feature_blob_data;
      for (int n = 0; n < batch_size; ++n) {
        datum.set_height(feature_blob->height());
        datum.set_width(feature_blob->width());
        datum.set_channels(feature_blob->channels());
        datum.clear_data();
        datum.clear_float_data();
        feature_blob_data = feature_blob->cpu_data() +
            feature_blob->offset(n);
        for (int d = 0; d < dim_features; ++d) {
          datum.add_float_data(feature_blob_data[d]);
        }
        string key_str = caffe::format_int(image_indices[i], 10);

        string out;
        CHECK(datum.SerializeToString(&out));
        txns.at(i)->Put(key_str, out);
        ++image_indices[i];
        if (image_indices[i] % 1000 == 0) {
          txns.at(i)->Commit();
          txns.at(i).reset(feature_dbs.at(i)->NewTransaction());
          LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
              " query images for feature blob " << blob_names[i];
        }
      }  // for (int n = 0; n < batch_size; ++n)
    }  // for (int i = 0; i < num_features; ++i)
  }  // for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index)
  // write the last batch
  for (int i = 0; i < num_features; ++i) {
    if (image_indices[i] % 1000 != 0) {
      txns.at(i)->Commit();
    }
    LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
        " query images for feature blob " << blob_names[i];
    feature_dbs.at(i)->Close();
  }

  LOG(ERROR)<< "Successfully extracted the features!";
  return 0;
}
