mxnet.io.PrefetchingIter¶

class mxnet.io.PrefetchingIter(iters, rename_data=None, rename_label=None)[source]¶

Performs pre-fetch for other data iterators.

This iterator will create another thread to perform iter_next and then store the data in memory. It potentially accelerates the data read, at the cost of more memory usage.

Parameters

iters (DataIter or list of DataIter) – The data iterators to be pre-fetched.
rename_data (None or list of dict) – The i-th element is a renaming map for the i-th iter, in the form of {‘original_name’ : ‘new_name’}. Should have one entry for each entry in iter[i].provide_data.
rename_label (None or list of dict) – Similar to rename_data.

Examples

>>> iter1 = mx.io.NDArrayIter({'data':mx.nd.ones((100,10))}, batch_size=25)
>>> iter2 = mx.io.NDArrayIter({'data':mx.nd.ones((100,10))}, batch_size=25)
>>> piter = mx.io.PrefetchingIter([iter1, iter2],
...                               rename_data=[{'data': 'data_1'}, {'data': 'data_2'}])
>>> print(piter.provide_data)
[DataDesc[data_1,(25, 10L),<type 'numpy.float32'>,NCHW],
 DataDesc[data_2,(25, 10L),<type 'numpy.float32'>,NCHW]]

__init__(iters, rename_data=None, rename_label=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(iters[, rename_data, rename_label])	Initialize self.
`getdata`()	Get data of current batch.
`getindex`()	Get index of the current batch.
`getlabel`()	Get label of the current batch.
`getpad`()	Get the number of padding examples in the current batch.
`iter_next`()	Move to the next batch.
`next`()	Get next data batch from iterator.
`reset`()	Reset the iterator to the begin of the data.

Attributes

`provide_data`
`provide_label`