Sampler¶

class paddle.io. Sampler ( data_source=None ) [source]

An abstract class to encapsulate methods and behaviors of samplers.

All sampler used by paddle.io.BatchSampler should be a subclass of paddle.io.Sampler, BatchSampler subclasses should implement following methods:

__iter__: return sample index iterably, which iterate over indices of dataset elements

__len__: the number of sample in data_source

Parameters: data_source (Dataset, optional) – this could be an instance of paddle.io.Dataset other Python object which implemented __len__ for Sampler to get indices as the range of dataset length. Default None.
Returns: an iterable object for sample indices iterating
Return type: Sampler

Examples

           >>> from paddle.io import Dataset, Sampler

>>> class RandomDataset(Dataset):
...     def __init__(self, num_samples):
...         self.num_samples = num_samples
...
...     def __getitem__(self, idx):
...         image = np.random.random([784]).astype('float32')
...         label = np.random.randint(0, 9, (1, )).astype('int64')
...         return image, label
...
...     def __len__(self):
...         return self.num_samples
...
>>> class MySampler(Sampler):
...     def __init__(self, data_source):
...         self.data_source = data_source
...
...     def __iter__(self):
...         return iter(range(len(self.data_source)))
...
...     def __len__(self):
...         return len(self.data_source)
...
>>> sampler = MySampler(data_source=RandomDataset(100))

>>> for index in sampler:
...     print(index)
0
1
2
...
99

          

see paddle.io.BatchSampler see paddle.io.DataLoader