Dataset shape property and debuggers

[clegaard]

Currently, the shape property of a dataset is determined by loading a single sample from the dataset.
This has the unintended effects when the dataset is inspected by a debugger like that in vscode, which evaluates the expression, which may potentially take several seconds if each sample is large.

datasetops/src/datasetops/dataset.py

Lines 276 to 299 in 45a6893

	@property
	def shape(self) -> Sequence[Shape]:
	"""Get the shape of a dataset item.
	Returns:
	Sequence[int] -- Item shapes
	"""
	if len(self) == 0:
	return _DEFAULT_SHAPE
	item = self.__getitem__(0)
	if hasattr(item, "__getitem__"):
	item_shape = []
	for i in item:
	if hasattr(i, "shape"): # numpy arrays
	item_shape.append(i.shape)
	elif hasattr(i, "size"): # PIL.Image.Image
	item_shape.append(np.array(i).shape)
	else:
	item_shape.append(_DEFAULT_SHAPE)
	return tuple(item_shape)
	return _DEFAULT_SHAPE

This begs the question of whether or not properties should have side effects? I relation to the subsampling operator, this messes a caching mechanism. If logging was enabled this would potentially cause unexpected log messages to be printed.

A solution could be caching the inferred shape, e.g. saving it to a private attribute _shape and having the property link to that value instead?