mindspore.dataset.SequentialSampler

class mindspore.dataset.SequentialSampler(start_index=None, num_samples=None)

Samples the dataset elements sequentially, same as not having a sampler.

Parameters

• start_index (int, optional) – Index to start sampling at. (dafault=None, start at first ID)

• num_samples (int, optional) – Number of elements to sample (default=None, all elements).

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "path/to/imagefolder_directory"
>>>
>>> # creates a SequentialSampler
>>> sampler = ds.SequentialSampler()
>>> data = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8, sampler=sampler)

get_num_samples()

All samplers can contain a numeric num_samples value (or it can be set to None). A child sampler can exist or be None. If a child sampler exists, then the child sampler count can be a numeric value or None. These conditions impact the resultant sampler count that is used. The following table shows the possible results from calling this function.

child sampler	num_samples	child_samples	result
T	x	y	min(x, y)
T	x	None	x
T	None	y	y
T	None	None	None
None	x	n/a	x
None	None	n/a	None

Returns

int, the number of samples, or None