def main(argv=None):
# Directories for logs and checkpoints
if FLAGS.dataset_name == 'MHAD':
if FLAGS.random_split_mode:
log_directory, ckpt_directory = config.get_directories_training(
FLAGS.dataset_name)
csv_filename = get_csv_filename(FLAGS.dataset_name)
csv_dir = './csv'
tfr_list, __, __ = csv_processor.get_random_percent_split(
csv_filename, csv_dir, ratio=0.75)
train_ckpt = '%smodel.ckpt' % ckpt_directory
utils.restore_logs(log_directory)
train(tfr_list, train_ckpt, log_directory, ckpt_directory)
else:
log_directory, ckpt_directory = config.get_directories_training(
FLAGS.dataset_name)
log_directory = os.path.join(log_directory,
str(FLAGS.test_subject))
ckpt_directory = os.path.join(ckpt_directory,
str(FLAGS.test_subject)) + '/'
csv_filename = get_csv_filename(FLAGS.dataset_name)
csv_dir = './csv'
tfr_list, __, __, __ = csv_processor.get_subject_wise_split(
csv_filename, csv_dir, FLAGS.test_subject)
train_ckpt = '%smodel.ckpt' % ckpt_directory
utils.restore_logs(log_directory)
print 'Currently processing for subject number %d' % FLAGS.test_subject
train(tfr_list, train_ckpt, log_directory, ckpt_directory)
elif FLAGS.dataset_name == 'UBC_easy' or FLAGS.dataset_name == 'forced_UBC_easy' \
or FLAGS.dataset_name == 'UBC_MHAD' or FLAGS.dataset_name == 'UBC_interpolated' \
or FLAGS.dataset_name == 'UBC_medium' or FLAGS.dataset_name == 'UBC_hard':
log_directory, ckpt_directory = config.get_directories_training(
FLAGS.dataset_name)
csv_filename = get_csv_filename(FLAGS.dataset_name)
tfr_list = csv_processor.get_train_list(csv_filename)
train_ckpt = '%smodel.ckpt' % ckpt_directory
utils.restore_logs(log_directory)
train(tfr_list, train_ckpt, log_directory, ckpt_directory)
def main(argv=None): utils.restore_logs(FLAGS.test_logs) test()
def main(argv=None):
utils.restore_logs(FLAGS.train_logs)
with tf.device('/gpu:0'):
train()
def main(argv=None): utils.restore_logs(FLAGS.train_logs) train()