def split_train_val(dataset: TaskSet,
val_split: float = 0.1) -> Tuple[TaskSet, TaskSet]:
"""Split train dataset into two datasets, one for training and one for validation.
:param dataset: A torch dataset, with .x and .y attributes.
:param val_split: Percentage to allocate for validation, between [0, 1[.
:return: A tuple a dataset, respectively for train and validation.
"""
random_state = np.random.RandomState(seed=1)
indexes = np.arange(len(dataset))
random_state.shuffle(indexes)
train_indexes = indexes[int(val_split * len(indexes)):]
val_indexes = indexes[:int(val_split * len(indexes))]
x_train, y_train, t_train = dataset.get_raw_samples(train_indexes)
train_dataset = TaskSet(x_train,
y_train,
t_train,
trsf=dataset.trsf,
data_type=dataset.data_type)
x_val, y_val, t_val = dataset.get_raw_samples(val_indexes)
val_dataset = TaskSet(x_val,
y_val,
t_val,
trsf=dataset.trsf,
data_type=dataset.data_type)
return train_dataset, val_dataset
def taskset_subset(taskset: TaskSet, indices: np.ndarray) -> TaskSet:
# x, y, t = taskset.get_raw_samples(indices)
x, y, t = taskset.get_raw_samples(indices)
# TODO: Not sure if/how to handle the `bounding_boxes` attribute here.
bounding_boxes = taskset.bounding_boxes
if bounding_boxes is not None:
bounding_boxes = bounding_boxes[indices]
return replace_taskset_attributes(
taskset, x=x, y=y, t=t, bounding_boxes=bounding_boxes
)
def test_get_raw_samples(nb_samples):
x = np.ones((10, 2, 2, 3))
y = np.ones((10, ))
t = np.ones((10, ))
base_set = TaskSet(x, y, t, None)
data, y_, t_ = base_set.get_raw_samples(indexes=range(nb_samples))
assert (x[:nb_samples] == data).all()
assert (y[:nb_samples] == y_).all()
assert (t[:nb_samples] == t_).all()