def test_dataloader(args):
test_dataset_file = "{0}/test_data_{1}.hdf5".format(args.locations["train_test_datadir"],args.region)
validation_loader = torch.utils.data.DataLoader(
data_io.ConcatDataset("test",args.nlevs, test_dataset_file, args.locations['normaliser_loc'], xvars=args.xvars,
yvars=args.yvars, yvars2=args.yvars2, data_frac=args.data_fraction, add_adv=False),
batch_size=args.batch_size, shuffle=False)
return validation_loader
def test_dataloader(self):
test_dataset_file = "{0}/test_data_{1}.hdf5".format(
locations["train_test_datadir"], region)
validation_loader = torch.utils.data.DataLoader(data_io.ConcatDataset(
"test", nlevs, test_dataset_file, overfit=True),
batch_size=batch_size,
shuffle=False)
return validation_loader
def train_dataloader(args):
train_dataset_file = "{0}/train_data_{1}.hdf5".format(
args.locations["train_test_datadir"], args.region)
concatDataset = data_io.ConcatDataset("train",
args.nlevs,
train_dataset_file,
args.locations['normaliser_loc'],
data_frac=args.data_fraction)
train_loader = torch.utils.data.DataLoader(concatDataset,
batch_size=args.batch_size,
shuffle=True)
return train_loader, concatDataset