train.py

"""Main training file for face recognition
"""
# MIT License
# 
# Copyright (c) 2018 Debayan Deb
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# 
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# 
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

import os
import sys
import time
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import argparse
import utils
import tflib
from network import Network
from tensorflow.contrib.tensorboard.plugins import projector
import evaluate

# Config File
parser = argparse.ArgumentParser()
parser.add_argument('--config_file', help='Path to training configuration file', type=str)
config_file = parser.parse_args().config_file
# I/O
config = utils.import_file(config_file, 'config')

trainset = utils.Dataset(config.splits_path +  '/train_' + str(config.fold_number) + '.txt')
trainset.images = utils.preprocess(trainset.images, config, True)

network = Network()
network.initialize(config, trainset.num_classes)


# Initalization for running
log_dir = utils.create_log_dir(config, config_file)
summary_writer = tf.summary.FileWriter(log_dir, network.graph)
if config.restore_model:
    network.restore_model(config.restore_model, config.restore_scopes)


# Load gallery and probe file_list
print('Loading images...')
probes = []
gal = []
with open(config.splits_path + '/fold_' + str(config.fold_number) + '/probe_1.txt' ,'r') as f:
    for line in f:
        probes.append(line.strip())

probe_set = evaluate.ImageSet(probes, config)
#probe_set.extract_features(network, len(probes))
#
with open(config.splits_path + '/fold_' + str(config.fold_number) + '/gal_1.txt', 'r') as f:
    for line in f:
        gal.append(line.strip())
gal_set = evaluate.ImageSet(gal, config)
#gal_set.extract_features(network, len(gal))

trainset.start_batch_queue(config, True)


#
# Main Loop
#
print('\nStart Training\n# epochs: %d\nepoch_size: %d\nbatch_size: %d\n'\
    % (config.num_epochs, config.epoch_size, config.batch_size))
global_step = 0
start_time = time.time()
for epoch in range(config.num_epochs):
    # Training
    for step in range(config.epoch_size):
        # Prepare input
        learning_rate = utils.get_updated_learning_rate(global_step, config)
        image_batch, label_batch = trainset.pop_batch_queue()


        wl, sm, global_step = network.train(image_batch, label_batch, learning_rate, config.keep_prob)

        # Display
        if step % config.summary_interval == 0:
            # visualize.scatter2D(_prelogits[:,:2], _label_batch, _pgrads[0][:,:2])
            duration = time.time() - start_time
            start_time = time.time()
            utils.display_info(epoch, step, duration, wl)
            summary_writer.add_summary(sm, global_step=global_step)

    
    # Testing
    print('Testing...')
    probe_set.extract_features(network, len(probes))
    gal_set.extract_features(network, len(gal))

    rank1, rank5 = evaluate.identify(log_dir, probe_set, gal_set)
    print('rank-1: %2.3f, rank-5: %2.3f' % (rank1[0], rank5[0]))
    
    # Output test result
    summary = tf.Summary()
    summary.value.add(tag='identification/rank1', simple_value=rank1[0])
    summary.value.add(tag='identification/rank5', simple_value=rank5[0])
    summary_writer.add_summary(summary, global_step)

    # Save the model
    network.save_model(log_dir, global_step)