pytorch_pfn_extras.dataloaders.dataloader.default_convert

pytorch_pfn_extras.dataloaders.dataloader.default_convert(data)

Convert each NumPy array element into a torch.Tensor.

If the input is a Sequence, Collection, or Mapping, it tries to convert each element inside to a torch.Tensor. If the input is not an NumPy array, it is left unchanged. This is used as the default function for collation when both batch_sampler and batch_size are NOT defined in DataLoader.

The general input type to output type mapping is similar to that of default_collate(). See the description there for more details.

Parameters:

data – a single data point to be converted

Examples

>>> # xdoctest: +SKIP
>>> # Example with `int`
>>> default_convert(0)
0
>>> # Example with NumPy array
>>> default_convert(np.array([0, 1]))
tensor([0, 1])
>>> # Example with NamedTuple
>>> Point = namedtuple('Point', ['x', 'y'])
>>> default_convert(Point(0, 0))
Point(x=0, y=0)
>>> default_convert(Point(np.array(0), np.array(0)))
Point(x=tensor(0), y=tensor(0))
>>> # Example with List
>>> default_convert([np.array([0, 1]), np.array([2, 3])])
[tensor([0, 1]), tensor([2, 3])]