torch.utils.data

class torch.utils.data.Dataset

An abstract class representing a Dataset.

All other datasets should subclass it. All subclasses should override __len__, that provides the size of the dataset, and __getitem__, supporting integer indexing in range from 0 to len(self) exclusive.

class torch.utils.data.TensorDataset(data_tensor, target_tensor)

Dataset wrapping data and target tensors.

Each sample will be retrieved by indexing both tensors along the first dimension.

Parameters:

• data_tensor (Tensor) – contains sample data.
• target_tensor (Tensor) – contains sample targets (labels).

class torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, num_workers=0, collate_fn=<function default_collate>, pin_memory=False, drop_last=False)

Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset.

Parameters:

• dataset (Dataset) – dataset from which to load the data.
• batch_size (int, optional) – how many samples per batch to load (default: 1).
• shuffle (bool, optional) – set to True to have the data reshuffled at every epoch (default: False).
• sampler (Sampler, optional) – defines the strategy to draw samples from the dataset. If specified, the shuffle argument is ignored.
• num_workers (int, optional) – how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process (default: 0)
• collate_fn (callable, optional) –
• pin_memory (bool, optional) –
• drop_last (bool, optional) – set to True to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If False and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: False)

class torch.utils.data.sampler.Sampler(data_source)

Base class for all Samplers.

Every Sampler subclass has to provide an __iter__ method, providing a way to iterate over indices of dataset elements, and a __len__ method that returns the length of the returned iterators.

class torch.utils.data.sampler.SequentialSampler(data_source)

Samples elements sequentially, always in the same order.

Parameters:data_source (Dataset) – dataset to sample from

class torch.utils.data.sampler.RandomSampler(data_source)

Samples elements randomly, without replacement.

Parameters:data_source (Dataset) – dataset to sample from

class torch.utils.data.sampler.SubsetRandomSampler(indices)

Samples elements randomly from a given list of indices, without replacement.

Parameters:indices (list) – a list of indices

class torch.utils.data.sampler.WeightedRandomSampler(weights, num_samples, replacement=True)

Samples elements from [0,..,len(weights)-1] with given probabilities (weights). :param weights: a list of weights, not necessary summing up to one :type weights: list :param num_samples: number of samples to draw :type num_samples: int