def test_misconfiguration():
with pytest.raises(MisconfigurationException, match="[0, 99]"):
SplitDataset(range(100), indices=[100])
with pytest.raises(MisconfigurationException, match="[0, 49]"):
SplitDataset(range(50), indices=[-1])
with pytest.raises(MisconfigurationException, match="[0, 49]"):
SplitDataset(list(range(50)) + list(range(50)), indices=[-1])
with pytest.raises(MisconfigurationException, match="[0, 99]"):
SplitDataset(list(range(50)) + list(range(50)), indices=[-1], use_duplicated_indices=True)
with pytest.raises(MisconfigurationException, match="indices should be a list"):
SplitDataset(list(range(100)), indices="not a list")
def _split_train_val(
train_dataset: Dataset,
val_split: float,
) -> Tuple[Any, Any]:
"""Utility function for splitting the training dataset into a disjoint subset of training samples and
validation samples.
Args:
train_dataset: A instance of a :class:`torch.utils.data.Dataset`.
val_split: A float between 0 and 1 determining the number fraction of samples that should go into the
validation split
Returns:
A tuple containing the training and validation datasets
"""
if not isinstance(val_split, float) or (isinstance(val_split, float)
and val_split > 1
or val_split < 0):
raise MisconfigurationException(
f"`val_split` should be a float between 0 and 1. Found {val_split}."
)
if isinstance(train_dataset, IterableInput):
raise MisconfigurationException(
"`val_split` should be `None` when the dataset is built with an IterableDataset."
)
val_num_samples = int(len(train_dataset) * val_split)
indices = list(range(len(train_dataset)))
np.random.shuffle(indices)
val_indices = indices[:val_num_samples]
train_indices = indices[val_num_samples:]
return (
SplitDataset(train_dataset,
train_indices,
running_stage=RunningStage.TRAINING,
use_duplicated_indices=True),
SplitDataset(train_dataset,
val_indices,
running_stage=RunningStage.VALIDATING,
use_duplicated_indices=True),
)
def test_split_dataset():
train_ds, val_ds = DataModule._split_train_val(range(100), val_split=0.1)
assert len(train_ds) == 90
assert len(val_ds) == 10
assert len(np.unique(train_ds.indices)) == len(train_ds.indices)
class Dataset:
def __init__(self):
self.data = [0, 1, 2]
self.name = "something"
self.is_passed_down = False
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return len(self.data)
split_dataset = SplitDataset(Dataset(), indices=[0])
assert split_dataset.name == "something"
split_dataset.is_passed_down = True
assert not split_dataset.dataset.is_passed_down
def test_split_dataset(tmpdir):
train_ds, val_ds = DataModule._split_train_val(range(100), val_split=0.1)
assert len(train_ds) == 90
assert len(val_ds) == 10
assert len(np.unique(train_ds.indices)) == len(train_ds.indices)
with pytest.raises(MisconfigurationException, match="[0, 99]"):
SplitDataset(range(100), indices=[100])
with pytest.raises(MisconfigurationException, match="[0, 49]"):
SplitDataset(range(50), indices=[-1])
with pytest.raises(MisconfigurationException, match="[0, 49]"):
SplitDataset(list(range(50)) + list(range(50)), indices=[-1])
with pytest.raises(MisconfigurationException, match="[0, 99]"):
SplitDataset(list(range(50)) + list(range(50)),
indices=[-1],
use_duplicated_indices=True)
class Dataset:
def __init__(self):
self.data = [0, 1, 2]
self.name = "something"
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return len(self.data)
split_dataset = SplitDataset(Dataset(), indices=[0])
assert split_dataset.name == "something"
assert split_dataset._INTERNAL_KEYS == ("dataset", "indices", "data")
split_dataset.is_passed_down = True
assert split_dataset.dataset.is_passed_down