mindspore.dataset.RandomSampler

class mindspore.dataset.RandomSampler(replacement=False, num_samples=None)

Samples the elements randomly.

Parameters

• replacement (bool, optional) – If True, put the sample ID back for the next draw. Default: False.

• num_samples (int, optional) – Number of elements to sample. Default: None , which means sample all elements.

Raises

• TypeError – If replacement is not of type bool.

• TypeError – If num_samples is not of type int.

• ValueError – If num_samples is a negative value.

Examples

>>> import mindspore.dataset as ds
>>> # creates a RandomSampler
>>> sampler = ds.RandomSampler()
>>> dataset = ds.ImageFolderDataset(image_folder_dataset_dir,
...                                 num_parallel_workers=8,
...                                 sampler=sampler)

add_child(sampler)

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

Parameters

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

Examples

>>> import mindspore.dataset as ds
>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3)
>>> sampler.add_child(ds.RandomSampler(num_samples=4))
>>> 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

>>> import mindspore.dataset as ds
>>> 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()

Get num_samples value of the current sampler instance. This parameter can be optionally passed in when defining the Sampler. Default: None. This method will return the num_samples value. If the current sampler has child samplers, it will continue to access the child samplers and process the obtained value according to certain rules.

The following table shows the various possible combinations, and the final results returned.

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

>>> import mindspore.dataset as ds
>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3)
>>> num_samplers = sampler.get_num_samples()