def main(argv): st = time.time() print 'start loading data' dataset_train = Dataset(g_.IMAGE_LIST_TRAIN, subtract_mean=True, name='train') dataset_val = Dataset(g_.IMAGE_LIST_VAL, subtract_mean=True, name='val') print 'done loading data, time=', time.time() - st train(dataset_train, dataset_val, FLAGS.weights, FLAGS.caffemodel)
def main(argv): st = time.time() print 'start loading data' dataset = Dataset(g_.IMAGE_LIST_TEST, subtract_mean=True, name='test') print 'done loading data, time=', time.time() - st test(dataset, FLAGS.weights)
def main(argv): st = time.time() print('start loading data') listfiles_train, labels_train = read_lists(g_.TRAIN_LOL) listfiles_val, labels_val = read_lists(g_.VAL_LOL) dataset_train = Dataset(listfiles_train, labels_train, subtract_mean=False, V=g_.NUM_VIEWS) dataset_val = Dataset(listfiles_val, labels_val, subtract_mean=False, V=g_.NUM_VIEWS) print('done loading data, time=', time.time() - st) train(dataset_train, dataset_val, FLAGS.weights, FLAGS.caffemodel)
def main(argv): st = time.time() print 'start loading data' listfiles, labels = read_lists(g_.TEST_LOL) dataset = Dataset(listfiles, labels, subtract_mean=False, V=g_.NUM_VIEWS) print 'done loading data, time=', time.time() - st test(dataset, FLAGS.weights)
data_path = config.data test = 0 with open(os.path.join(data_path, 'test.txt'), 'r') as f: for line in f: if os.path.exists(line.split()[0]): pass else: test += 1 print(line) print('start loading data') listfiles_test, labels_test = read_lists( os.path.join(data_path, 'test.txt')) dataset_test = Dataset(listfiles_test, labels_test, subtract_mean=False, V=config.num_views) if not config.test: LOSS_LOGGER = Logger("{}_loss".format(config.name)) ACC_LOGGER = Logger("{}_acc".format(config.name)) listfiles_train, labels_train = read_lists( os.path.join(data_path, 'train.txt')) dataset_train = Dataset(listfiles_train, labels_train, subtract_mean=False, V=config.num_views) train(dataset_train, dataset_test, config.pretrained_network_file) if config.weights == -1: config = add_to_config(config, 'weights', config.max_epoch)