def main(args):
logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
with tf.compat.v1.name_scope('create_inputs'):
if args.format == "mseed_array":
data_reader = DataReader_mseed_array(
data_dir=args.data_dir,
data_list=args.data_list,
stations=args.stations,
amplitude=args.amplitude,
highpass_filter=args.highpass_filter,
)
else:
data_reader = DataReader_pred(
format=args.format,
data_dir=args.data_dir,
data_list=args.data_list,
hdf5_file=args.hdf5_file,
hdf5_group=args.hdf5_group,
amplitude=args.amplitude,
highpass_filter=args.highpass_filter,
)
pred_fn(args, data_reader, log_dir=args.result_dir)
return
def main(args):
logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
coord = tf.train.Coordinator()
if args.mode == "train":
with tf.compat.v1.name_scope('create_inputs'):
data_reader = DataReader(
data_dir=args.train_dir,
data_list=args.train_list,
mask_window=0.4,
queue_size=args.batch_size * 3,
coord=coord)
if args.valid_list is not None:
data_reader_valid = DataReader(
data_dir=args.valid_dir,
data_list=args.valid_list,
mask_window=0.4,
queue_size=args.batch_size * 2,
coord=coord)
logging.info(
"Dataset size: train {}, valid {}".format(data_reader.num_data, data_reader_valid.num_data))
else:
data_reader_valid = None
logging.info("Dataset size: train {}".format(data_reader.num_data))
train_fn(args, data_reader, data_reader_valid)
elif args.mode == "valid" or args.mode == "test":
with tf.compat.v1.name_scope('create_inputs'):
data_reader = DataReader_test(
data_dir=args.data_dir,
data_list=args.data_list,
mask_window=0.4,
queue_size=args.batch_size * 10,
coord=coord)
valid_fn(args, data_reader)
elif args.mode == "pred":
with tf.compat.v1.name_scope('create_inputs'):
if args.input_mseed:
data_reader = DataReader_mseed(
data_dir=args.data_dir,
data_list=args.data_list,
queue_size=args.batch_size * 10,
coord=coord,
input_length=args.input_length)
else:
data_reader = DataReader_pred(
data_dir=args.data_dir,
data_list=args.data_list,
queue_size=args.batch_size * 10,
coord=coord,
input_length=args.input_length)
pred_fn(args, data_reader, log_dir=args.output_dir)
else:
print("mode should be: train, valid, test, pred or debug")
return
def main(args):
logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
with tf.compat.v1.name_scope('create_inputs'):
data_reader = DataReader_pred(format=args.format,
signal_dir=args.data_dir,
signal_list=args.data_list,
sampling_rate=args.sampling_rate)
logging.info("Dataset Size: {}".format(data_reader.n_signal))
pred_fn(args, data_reader, log_dir=args.output_dir)
return 0