mindspore.dataset.PKSampler

class mindspore.dataset.PKSampler(num_val, num_class=None, shuffle=False, class_column='label', num_samples=None)

Samples K elements for each P class in the dataset.

Parameters

• num_val (int) – Number of elements to sample for each class.

• num_class (int, optional) – Number of classes to sample (default=None, all classes). The parameter does not supported to specify currently.

• shuffle (bool, optional) – If True, the class IDs are shuffled (default=False).

• class_column (str, optional) – Name of column with class labels for MindDataset (default=’label’).

• num_samples (int, optional) – The number of samples to draw (default=None, all elements).

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "path/to/imagefolder_directory"
>>>
>>> # creates a PKSampler that will get 3 samples from every class.
>>> sampler = ds.PKSampler(3)
>>> data = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8, sampler=sampler)

Raises

• ValueError – If num_val is not positive.

• NotImplementedError – If num_class is not None.

• ValueError – If shuffle is not boolean.

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