mindspore.dataset.SubsetSampler
- class mindspore.dataset.SubsetSampler(indices, num_samples=None)[source]
Samples the elements from a sequence of indices.
- Parameters
indices (Any iterable Python object but string) – A sequence of indices.
num_samples (int, optional) – Number of elements to sample (default=None, which means sample all elements).
- Raises
TypeError – If elements of indices are not of type number.
TypeError – If num_samples is not of type int.
ValueError – If num_samples is a negative value.
Examples
>>> indices = [0, 1, 2, 3, 4, 5] >>> >>> # creates a SubsetSampler, will sample from the provided indices >>> sampler = ds.SubsetSampler(indices) >>> 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(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=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
>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3) >>> sampler.add_child(ds.RandomSampler(num_samples=2)) >>> child_sampler = sampler.get_child()