def margin(self, margin: float) -> None:
if not isinstance(margin, float):
raise e.TypeError("`margin` should be a float")
if margin <= 0:
raise e.ValueError("`margin` should be greater than 0")
self._margin = margin
def batch_size(self, batch_size: int) -> None:
if not isinstance(batch_size, int):
raise e.TypeError("`batch_size` should be a integer")
if batch_size <= 0:
raise e.ValueError("`batch_size` should be greater than 0")
self._batch_size = batch_size
def test_value_error():
new_exception = exception.ValueError("error")
try:
raise new_exception
except exception.ValueError:
pass
def __init__(
self,
data: np.array,
labels: np.array,
n_pairs: Optional[int] = 2,
batch_size: Optional[int] = 1,
input_shape: Optional[Tuple[int, ...]] = None,
normalize: Optional[Tuple[int, int]] = (0, 1),
shuffle: Optional[bool] = True,
seed: Optional[int] = 0,
):
"""Initialization method.
Args:
data: Array of samples.
labels: Array of labels.
n_pairs: Number of pairs.
batch_size: Batch size.
input_shape: Shape of the reshaped array.
normalize: Normalization bounds.
shuffle: Whether data should be shuffled or not.
seed: Provides deterministic traits when using `random` module.
"""
logger.info("Overriding class: Dataset -> BalancedPairDataset.")
super(BalancedPairDataset, self).__init__(batch_size, input_shape,
normalize, shuffle, seed)
try:
# Checks if supplied labels are not equal
assert not np.all(labels == labels[0])
except:
raise e.ValueError("`labels` should have distinct values")
# Amount of pairs
self.n_pairs = n_pairs
data = self.preprocess(data)
pairs = self.create_pairs(data, labels)
self._build(pairs)
logger.info("Class overrided.")
def loss_type(self, loss_type: str) -> None:
if loss_type not in ["hard", "semi-hard"]:
raise e.ValueError("`loss_type` should be `hard` or `semi-hard`")
self._loss_type = loss_type
def distance(self, distance: str) -> None:
if distance not in ["L1", "L2", "squared-L2", "angular"]:
raise e.ValueError("`distance` should be `L1`, `L2` or `angular`")
self._distance = distance
def distance(self, distance: str) -> None:
if distance not in ["concat", "diff"]:
raise e.ValueError("`distance` should be `concat`, or `diff`")
self._distance = distance