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, sample all classes). The parameter does not supported to specify currently.

  • shuffle (bool, optional) – If True, the class IDs are shuffled, otherwise it will not be 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, which means sample 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
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()

Get the child sampler of given sampler.

Returns

Sampler, The child sampler of given sampler.

Examples

>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3)
>>> sampler.add_child(ds.RandomSampler(num_samples=2))
>>> child_sampler = sampler.get_child()
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.

Examples

>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3)
>>> num_samplers = sampler.get_num_samples()