mindspore.dataset.PKSampler

class mindspore.dataset.PKSampler(num_val, num_class=None, shuffle=False, class_column=label, num_samples=None)[source]

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

>>> # creates a PKSampler that will get 3 samples from every class.
>>> sampler = ds.PKSampler(3)
>>> dataset = ds.ImageFolderDataset(image_folder_dataset_dir,
...                                 num_parallel_workers=8,
...                                 sampler=sampler)

Raises

TypeError – If num_val is not a positive value.
TypeError – If shuffle is not a boolean value.
TypeError – If class_column is not a str value.
TypeError – If num_samples is not an integer value.
NotImplementedError – If num_class is not None.
RuntimeError – If num_val is not a positive value.
RuntimeError – If num_samples is a negative value.

add_child(sampler)

Add a sub-sampler for given sampler. The sub-sampler will receive all data from the output of parent sampler and apply its sample logic to return new samples.

Parameters: sampler (Sampler) – Object used to choose samples from the dataset. Only builtin samplers(DistributedSampler, PKSampler, RandomSampler, SequentialSampler, SubsetRandomSampler, WeightedRandomSampler) are supported.

Examples

>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3)
>>> sampler.add_child(ds.RandomSampler(num_samples=2))
>>> dataset = ds.Cifar10Dataset(cifar10_dataset_dir, sampler=sampler)

get_child(): add a child 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

parse()[source]: Parse the sampler.

parse_child(): Parse the child sampler.

parse_child_for_minddataset(): Parse the child sampler for MindRecord.

parse_for_minddataset()[source]: Parse the sampler for MindRecord.