mxnet.contrib.io

Contrib data iterators for common data formats.

Classes

DataLoaderIter(loader[, data_name, ...])

Returns an iterator for mx.gluon.data.Dataloader so gluon dataloader can be used in symbolic module.

class mxnet.contrib.io.DataLoaderIter(loader, data_name='data', label_name='softmax_label', dtype='float32')

Bases: DataIter

Returns an iterator for mx.gluon.data.Dataloader so gluon dataloader can be used in symbolic module.

Parameters:

• loader (mxnet.gluon.data.Dataloader) – Gluon dataloader instance

• data_name (str, optional) – The data name.

• label_name (str, optional) – The label name.

• dtype (str, optional) – The dtype specifier, can be float32 or float16

Examples

>>> import mxnet as mx
>>> from mxnet.gluon.data.vision import MNIST
>>> from mxnet.gluon.data import DataLoader
>>> train_dataset = MNIST(train=True)
>>> train_data = mx.gluon.data.DataLoader(train_dataset, 32, shuffle=True, num_workers=4)
>>> dataiter = mx.io.DataloaderIter(train_data)
>>> for batch in dataiter:
...     batch.data[0].shape
...
(32L, 28L, 28L, 1L)

getdata()

Get data of current batch.

Returns:

The data of the current batch.

Return type:

list of NDArray

getindex()

Get index of the current batch.

Returns:

index – The indices of examples in the current batch.

Return type:

numpy.array

getlabel()

Get label of the current batch.

Returns:

The label of the current batch.

Return type:

list of NDArray

getpad()

Get the number of padding examples in the current batch.

Returns:

Number of padding examples in the current batch.

Return type:

int

iter_next()

Move to the next batch.

Returns:

Whether the move is successful.

Return type:

boolean

reset()

Reset the iterator to the begin of the data.