Caffe LFW Training

convert_lfw_siamese_data.cpp

//
// This script converts the lfw dataset to the leveldb format used
// by caffe to train siamese network.
// Usage:
//    convert_lfw_data input_image_file input_label_file output_db_file

#include <fstream>  // NOLINT(readability/streams)
#include <string>

#include "glog/logging.h"
#include "google/protobuf/text_format.h"
#include "leveldb/db.h"
#include "stdint.h"

#include "caffe/proto/caffe.pb.h"
#include "caffe/util/math_functions.hpp"

using namespace std;

void read_image(std::ifstream* image_file, std::ifstream* label_file,
                uint32_t index, uint32_t rows, uint32_t cols,
                char* pixels, char* label) {
  image_file->seekg(index * rows * cols + 12);
  image_file->read(pixels, rows * cols);
  image_file->read(pixels + (rows * cols), rows * cols);
  label_file->seekg(index + 8);
  label_file->read(label, 1);
}

void convert_dataset(const char* image_filename, const char* label_filename,
        const char* db_filename) {
  // Open files
  std::ifstream image_file(image_filename, std::ios::in | std::ios::binary);
  std::ifstream label_file(label_filename, std::ios::in | std::ios::binary);
  CHECK(image_file) << "Unable to open file " << image_filename;
  CHECK(label_file) << "Unable to open file " << label_filename;
  // Read the magic and the meta data
  int32_t num_items;
  int32_t num_labels;
  uint32_t rows;
  uint32_t cols;

  image_file.read(reinterpret_cast<char*>(&num_items), 4);
  label_file.read(reinterpret_cast<char*>(&num_labels), 4);

  CHECK_EQ(num_items, num_labels);
  image_file.read(reinterpret_cast<char*>(&rows), 4);
  image_file.read(reinterpret_cast<char*>(&cols), 4);

  cout << num_items << ", " << num_labels << endl;
  cout << rows << ", " << cols << endl;

  // Open leveldb
  leveldb::DB* db;
  leveldb::Options options;
  options.create_if_missing = true;
  options.error_if_exists = true;
  leveldb::Status status = leveldb::DB::Open(
      options, db_filename, &db);
  CHECK(status.ok()) << "Failed to open leveldb " << db_filename
      << ". Is it already existing?";

  char label_i;
  char* pixels = new char[2 * rows * cols];
  const int kMaxKeyLength = 10;
  char key[kMaxKeyLength];
  std::string value;

  caffe::Datum datum;
  datum.set_channels(2);  // one channel for each image in the pair
  datum.set_height(rows);
  datum.set_width(cols);
  LOG(INFO) << "A total of " << num_items << " items.";
  LOG(INFO) << "Rows: " << rows << " Cols: " << cols;
  for (int itemid = 0; itemid < num_items; ++itemid) {

    read_image( &image_file,
                &label_file,
                itemid,
                rows,
                cols,
                pixels,
                &label_i );

    datum.set_data(pixels, 2*rows*cols);
    datum.set_label(label_i);
    datum.SerializeToString(&value);
    snprintf(key, kMaxKeyLength, "%08d", itemid);
    db->Put(leveldb::WriteOptions(), std::string(key), value);
  }

  delete db;
  delete pixels;
}

int main(int argc, char** argv) {
  if (argc != 4) {
    printf("This script converts the MNIST dataset to the leveldb format used\n"
           "by caffe to train a siamese network.\n"
           "Usage:\n"
           "    convert_mnist_data input_image_file input_label_file "
           "output_db_file\n"
           "The MNIST dataset could be downloaded at\n"
           "    http://yann.lecun.com/exdb/mnist/\n"
           "You should gunzip them after downloading.\n");
  } else {
    google::InitGoogleLogging(argv[0]);
    convert_dataset(argv[1], argv[2], argv[3]);
  }
  return 0;
}

lfw_siamese_solver.prototxt

# The train/test net protocol buffer definition
net: "examples/lfw_siamese/lfw_siamese_train_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of lfw_01, we have test batch size 100 and 6 test iterations,
# covering the full 600 testing images.
test_iter: 6
# Carry out testing every 500 training iterations.
test_interval: 500
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.001
momentum: 0.9
weight_decay: 0.0000
# The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 50000
# snapshot intermediate results
snapshot: 5000
snapshot_prefix: "examples/siamese/mnist_siamese"
# solver mode: CPU or GPU
solver_mode: GPU

lfw_siamese_train_test.prototxt

name: "lfw_siamese_train_test"
layer {
  name: "pair_data"
  type: "Data"
  top: "pair_data"
  top: "sim"
  include {
    phase: TRAIN
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "examples/lfw_siamese/01_lfw_train_leveldb"
    batch_size: 64
  }
}
layer {
  name: "pair_data"
  type: "Data"
  top: "pair_data"
  top: "sim"
  include {
    phase: TEST
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "examples/lfw_siamese/01_lfw_test_leveldb"
    batch_size: 100
  }
}
layer {
  name: "slice_pair"
  type: "Slice"
  bottom: "pair_data"
  top: "data"
  top: "data_p"
  slice_param {
    slice_dim: 1
    slice_point: 1
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    name: "conv1_w"
    lr_mult: 1
  }
  param {
    name: "conv1_b"
    lr_mult: 2
  }
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    name: "conv2_w"
    lr_mult: 1
  }
  param {
    name: "conv2_b"
    lr_mult: 2
  }
  convolution_param {
    num_output: 50
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool2"
  top: "ip1"
  param {
    name: "ip1_w"
    lr_mult: 1
  }
  param {
    name: "ip1_b"
    lr_mult: 2
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    name: "ip2_w"
    lr_mult: 1
  }
  param {
    name: "ip2_b"
    lr_mult: 2
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "feat"
  type: "InnerProduct"
  bottom: "ip2"
  top: "feat"
  param {
    name: "feat_w"
    lr_mult: 1
  }
  param {
    name: "feat_b"
    lr_mult: 2
  }
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv1_p"
  type: "Convolution"
  bottom: "data_p"
  top: "conv1_p"
  param {
    name: "conv1_w"
    lr_mult: 1
  }
  param {
    name: "conv1_b"
    lr_mult: 2
  }
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1_p"
  type: "Pooling"
  bottom: "conv1_p"
  top: "pool1_p"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_p"
  type: "Convolution"
  bottom: "pool1_p"
  top: "conv2_p"
  param {
    name: "conv2_w"
    lr_mult: 1
  }
  param {
    name: "conv2_b"
    lr_mult: 2
  }
  convolution_param {
    num_output: 50
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool2_p"
  type: "Pooling"
  bottom: "conv2_p"
  top: "pool2_p"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "ip1_p"
  type: "InnerProduct"
  bottom: "pool2_p"
  top: "ip1_p"
  param {
    name: "ip1_w"
    lr_mult: 1
  }
  param {
    name: "ip1_b"
    lr_mult: 2
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1_p"
  type: "ReLU"
  bottom: "ip1_p"
  top: "ip1_p"
}
layer {
  name: "ip2_p"
  type: "InnerProduct"
  bottom: "ip1_p"
  top: "ip2_p"
  param {
    name: "ip2_w"
    lr_mult: 1
  }
  param {
    name: "ip2_b"
    lr_mult: 2
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "feat_p"
  type: "InnerProduct"
  bottom: "ip2_p"
  top: "feat_p"
  param {
    name: "feat_w"
    lr_mult: 1
  }
  param {
    name: "feat_b"
    lr_mult: 2
  }
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "loss"
  type: "ContrastiveLoss"
  bottom: "feat"
  bottom: "feat_p"
  bottom: "sim"
  top: "loss"
  contrastive_loss_param {
    margin: 1
  }
}

train_lfw_siamese.sh

#!/usr/bin/env sh

TOOLS=./build/tools

$TOOLS/caffe train --solver=examples/lfw_siamese/lfw_siamese_solver.prototxt

I download your convert_lfw_siamese_data.cpp. But i don't know how to create training database. How sign label for image?