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"""Data iterators for common data formats."""
from __future__ import absolute_import
from collections import OrderedDict, namedtuple
import sys
import ctypes
import logging
import threading
try:
import h5py
except ImportError:
h5py = None
import numpy as np
from .base import _LIB
from .base import c_str_array, mx_uint, py_str
from .base import DataIterHandle, NDArrayHandle
from .base import mx_real_t
from .base import check_call, build_param_doc as _build_param_doc
from .ndarray import NDArray
from .ndarray.sparse import CSRNDArray
from .ndarray.sparse import array as sparse_array
from .ndarray import _ndarray_cls
from .ndarray import array
from .ndarray import concatenate
from .ndarray import arange
from .ndarray.random import shuffle as random_shuffle
[docs]class DataDesc(namedtuple('DataDesc', ['name', 'shape'])):
"""DataDesc is used to store name, shape, type and layout
information of the data or the label.
The `layout` describes how the axes in `shape` should be interpreted,
for example for image data setting `layout=NCHW` indicates
that the first axis is number of examples in the batch(N),
C is number of channels, H is the height and W is the width of the image.
For sequential data, by default `layout` is set to ``NTC``, where
N is number of examples in the batch, T the temporal axis representing time
and C is the number of channels.
Parameters
----------
cls : DataDesc
The class.
name : str
Data name.
shape : tuple of int
Data shape.
dtype : np.dtype, optional
Data type.
layout : str, optional
Data layout.
"""
def __new__(cls, name, shape, dtype=mx_real_t, layout='NCHW'): # pylint: disable=super-on-old-class
ret = super(cls, DataDesc).__new__(cls, name, shape)
ret.dtype = dtype
ret.layout = layout
return ret
def __repr__(self):
return "DataDesc[%s,%s,%s,%s]" % (self.name, self.shape, self.dtype,
self.layout)
@staticmethod
[docs] def get_batch_axis(layout):
"""Get the dimension that corresponds to the batch size.
When data parallelism is used, the data will be automatically split and
concatenated along the batch-size dimension. Axis can be -1, which means
the whole array will be copied for each data-parallelism device.
Parameters
----------
layout : str
layout string. For example, "NCHW".
Returns
-------
int
An axis indicating the batch_size dimension.
"""
if layout is None:
return 0
return layout.find('N')
@staticmethod
[docs] def get_list(shapes, types):
"""Get DataDesc list from attribute lists.
Parameters
----------
shapes : a tuple of (name_, shape_)
types : a tuple of (name_, np.dtype)
"""
if types is not None:
type_dict = dict(types)
return [DataDesc(x[0], x[1], type_dict[x[0]]) for x in shapes]
else:
return [DataDesc(x[0], x[1]) for x in shapes]
[docs]class DataBatch(object):
"""A data batch.
MXNet's data iterator returns a batch of data for each `next` call.
This data contains `batch_size` number of examples.
If the input data consists of images, then shape of these images depend on
the `layout` attribute of `DataDesc` object in `provide_data` parameter.
If `layout` is set to 'NCHW' then, images should be stored in a 4-D matrix
of shape ``(batch_size, num_channel, height, width)``.
If `layout` is set to 'NHWC' then, images should be stored in a 4-D matrix
of shape ``(batch_size, height, width, num_channel)``.
The channels are often in RGB order.
Parameters
----------
data : list of `NDArray`, each array containing `batch_size` examples.
A list of input data.
label : list of `NDArray`, each array often containing a 1-dimensional array. optional
A list of input labels.
pad : int, optional
The number of examples padded at the end of a batch. It is used when the
total number of examples read is not divisible by the `batch_size`.
These extra padded examples are ignored in prediction.
index : numpy.array, optional
The example indices in this batch.
bucket_key : int, optional
The bucket key, used for bucketing module.
provide_data : list of `DataDesc`, optional
A list of `DataDesc` objects. `DataDesc` is used to store
name, shape, type and layout information of the data.
The *i*-th element describes the name and shape of ``data[i]``.
provide_label : list of `DataDesc`, optional
A list of `DataDesc` objects. `DataDesc` is used to store
name, shape, type and layout information of the label.
The *i*-th element describes the name and shape of ``label[i]``.
"""
def __init__(self, data, label=None, pad=None, index=None,
bucket_key=None, provide_data=None, provide_label=None):
if data is not None:
assert isinstance(data, (list, tuple)), "Data must be list of NDArrays"
if label is not None:
assert isinstance(label, (list, tuple)), "Label must be list of NDArrays"
self.data = data
self.label = label
self.pad = pad
self.index = index
self.bucket_key = bucket_key
self.provide_data = provide_data
self.provide_label = provide_label
def __str__(self):
data_shapes = [d.shape for d in self.data]
if self.label:
label_shapes = [l.shape for l in self.label]
else:
label_shapes = None
return "{}: data shapes: {} label shapes: {}".format(
self.__class__.__name__,
data_shapes,
label_shapes)
[docs]class DataIter(object):
"""The base class for an MXNet data iterator.
All I/O in MXNet is handled by specializations of this class. Data iterators
in MXNet are similar to standard-iterators in Python. On each call to `next`
they return a `DataBatch` which represents the next batch of data. When
there is no more data to return, it raises a `StopIteration` exception.
Parameters
----------
batch_size : int, optional
The batch size, namely the number of items in the batch.
See Also
--------
NDArrayIter : Data-iterator for MXNet NDArray or numpy-ndarray objects.
CSVIter : Data-iterator for csv data.
LibSVMIter : Data-iterator for libsvm data.
ImageIter : Data-iterator for images.
"""
def __init__(self, batch_size=0):
self.batch_size = batch_size
def __iter__(self):
return self
[docs] def reset(self):
"""Reset the iterator to the begin of the data."""
pass
[docs] def next(self):
"""Get next data batch from iterator.
Returns
-------
DataBatch
The data of next batch.
Raises
------
StopIteration
If the end of the data is reached.
"""
if self.iter_next():
return DataBatch(data=self.getdata(), label=self.getlabel(), \
pad=self.getpad(), index=self.getindex())
else:
raise StopIteration
def __next__(self):
return self.next()
[docs] def iter_next(self):
"""Move to the next batch.
Returns
-------
boolean
Whether the move is successful.
"""
pass
[docs] def getdata(self):
"""Get data of current batch.
Returns
-------
list of NDArray
The data of the current batch.
"""
pass
[docs] def getlabel(self):
"""Get label of the current batch.
Returns
-------
list of NDArray
The label of the current batch.
"""
pass
[docs] def getindex(self):
"""Get index of the current batch.
Returns
-------
index : numpy.array
The indices of examples in the current batch.
"""
return None
[docs] def getpad(self):
"""Get the number of padding examples in the current batch.
Returns
-------
int
Number of padding examples in the current batch.
"""
pass
[docs]class ResizeIter(DataIter):
"""Resize a data iterator to a given number of batches.
Parameters
----------
data_iter : DataIter
The data iterator to be resized.
size : int
The number of batches per epoch to resize to.
reset_internal : bool
Whether to reset internal iterator on ResizeIter.reset.
Examples
--------
>>> nd_iter = mx.io.NDArrayIter(mx.nd.ones((100,10)), batch_size=25)
>>> resize_iter = mx.io.ResizeIter(nd_iter, 2)
>>> for batch in resize_iter:
... print(batch.data)
[]
[]
"""
def __init__(self, data_iter, size, reset_internal=True):
super(ResizeIter, self).__init__()
self.data_iter = data_iter
self.size = size
self.reset_internal = reset_internal
self.cur = 0
self.current_batch = None
self.provide_data = data_iter.provide_data
self.provide_label = data_iter.provide_label
self.batch_size = data_iter.batch_size
if hasattr(data_iter, 'default_bucket_key'):
self.default_bucket_key = data_iter.default_bucket_key
def reset(self):
self.cur = 0
if self.reset_internal:
self.data_iter.reset()
def iter_next(self):
if self.cur == self.size:
return False
try:
self.current_batch = self.data_iter.next()
except StopIteration:
self.data_iter.reset()
self.current_batch = self.data_iter.next()
self.cur += 1
return True
def getdata(self):
return self.current_batch.data
def getlabel(self):
return self.current_batch.label
def getindex(self):
return self.current_batch.index
def getpad(self):
return self.current_batch.pad
[docs]class PrefetchingIter(DataIter):
"""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),,NCHW],
DataDesc[data_2,(25, 10L),,NCHW]]
"""
def __init__(self, iters, rename_data=None, rename_label=None):
super(PrefetchingIter, self).__init__()
if not isinstance(iters, list):
iters = [iters]
self.n_iter = len(iters)
assert self.n_iter > 0
self.iters = iters
self.rename_data = rename_data
self.rename_label = rename_label
self.batch_size = self.provide_data[0][1][0]
self.data_ready = [threading.Event() for i in range(self.n_iter)]
self.data_taken = [threading.Event() for i in range(self.n_iter)]
for i in self.data_taken:
i.set()
self.started = True
self.current_batch = [None for i in range(self.n_iter)]
self.next_batch = [None for i in range(self.n_iter)]
def prefetch_func(self, i):
"""Thread entry"""
while True:
self.data_taken[i].wait()
if not self.started:
break
try:
self.next_batch[i] = self.iters[i].next()
except StopIteration:
self.next_batch[i] = None
self.data_taken[i].clear()
self.data_ready[i].set()
self.prefetch_threads = [threading.Thread(target=prefetch_func, args=[self, i]) \
for i in range(self.n_iter)]
for thread in self.prefetch_threads:
thread.setDaemon(True)
thread.start()
def __del__(self):
self.started = False
for i in self.data_taken:
i.set()
for thread in self.prefetch_threads:
thread.join()
@property
def provide_data(self):
if self.rename_data is None:
return sum([i.provide_data for i in self.iters], [])
else:
return sum([[
DataDesc(r[x.name], x.shape, x.dtype)
if isinstance(x, DataDesc) else DataDesc(*x)
for x in i.provide_data
] for r, i in zip(self.rename_data, self.iters)], [])
@property
def provide_label(self):
if self.rename_label is None:
return sum([i.provide_label for i in self.iters], [])
else:
return sum([[
DataDesc(r[x.name], x.shape, x.dtype)
if isinstance(x, DataDesc) else DataDesc(*x)
for x in i.provide_label
] for r, i in zip(self.rename_label, self.iters)], [])
def reset(self):
for i in self.data_ready:
i.wait()
for i in self.iters:
i.reset()
for i in self.data_ready:
i.clear()
for i in self.data_taken:
i.set()
def iter_next(self):
for i in self.data_ready:
i.wait()
if self.next_batch[0] is None:
for i in self.next_batch:
assert i is None, "Number of entry mismatches between iterators"
return False
else:
for batch in self.next_batch:
assert batch.pad == self.next_batch[0].pad, \
"Number of entry mismatches between iterators"
self.current_batch = DataBatch(sum([batch.data for batch in self.next_batch], []),
sum([batch.label for batch in self.next_batch], []),
self.next_batch[0].pad,
self.next_batch[0].index,
provide_data=self.provide_data,
provide_label=self.provide_label)
for i in self.data_ready:
i.clear()
for i in self.data_taken:
i.set()
return True
def next(self):
if self.iter_next():
return self.current_batch
else:
raise StopIteration
def getdata(self):
return self.current_batch.data
def getlabel(self):
return self.current_batch.label
def getindex(self):
return self.current_batch.index
def getpad(self):
return self.current_batch.pad
def _init_data(data, allow_empty, default_name):
"""Convert data into canonical form."""
assert (data is not None) or allow_empty
if data is None:
data = []
if isinstance(data, (np.ndarray, NDArray, h5py.Dataset)
if h5py else (np.ndarray, NDArray)):
data = [data]
if isinstance(data, list):
if not allow_empty:
assert(len(data) > 0)
if len(data) == 1:
data = OrderedDict([(default_name, data[0])]) # pylint: disable=redefined-variable-type
else:
data = OrderedDict( # pylint: disable=redefined-variable-type
[('_%d_%s' % (i, default_name), d) for i, d in enumerate(data)])
if not isinstance(data, dict):
raise TypeError("Input must be NDArray, numpy.ndarray, h5py.Dataset " + \
"a list of them or dict with them as values")
for k, v in data.items():
if not isinstance(v, (NDArray, h5py.Dataset) if h5py else NDArray):
try:
data[k] = array(v)
except:
raise TypeError(("Invalid type '%s' for %s, " % (type(v), k)) + \
"should be NDArray, numpy.ndarray or h5py.Dataset")
return list(sorted(data.items()))
def _has_instance(data, dtype):
"""Return True if ``data`` has instance of ``dtype``.
This function is called after _init_data.
``data`` is a list of (str, NDArray)"""
for item in data:
_, arr = item
if isinstance(arr, dtype):
return True
return False
def _shuffle(data, idx):
"""Shuffle the data."""
shuffle_data = []
for k, v in data:
if (isinstance(v, h5py.Dataset) if h5py else False):
shuffle_data.append((k, v))
elif isinstance(v, CSRNDArray):
shuffle_data.append((k, sparse_array(v.asscipy()[idx], v.context)))
else:
shuffle_data.append((k, array(v.asnumpy()[idx], v.context)))
return shuffle_data
[docs]class NDArrayIter(DataIter):
"""Returns an iterator for ``mx.nd.NDArray``, ``numpy.ndarray``, ``h5py.Dataset``
``mx.nd.sparse.CSRNDArray`` or ``scipy.sparse.csr_matrix``.
Example usage:
----------
>>> data = np.arange(40).reshape((10,2,2))
>>> labels = np.ones([10, 1])
>>> dataiter = mx.io.NDArrayIter(data, labels, 3, True, last_batch_handle='discard')
>>> for batch in dataiter:
... print batch.data[0].asnumpy()
... batch.data[0].shape
...
[[[ 36. 37.]
[ 38. 39.]]
[[ 16. 17.]
[ 18. 19.]]
[[ 12. 13.]
[ 14. 15.]]]
(3L, 2L, 2L)
[[[ 32. 33.]
[ 34. 35.]]
[[ 4. 5.]
[ 6. 7.]]
[[ 24. 25.]
[ 26. 27.]]]
(3L, 2L, 2L)
[[[ 8. 9.]
[ 10. 11.]]
[[ 20. 21.]
[ 22. 23.]]
[[ 28. 29.]
[ 30. 31.]]]
(3L, 2L, 2L)
>>> dataiter.provide_data # Returns a list of `DataDesc`
[DataDesc[data,(3, 2L, 2L),,NCHW]]
>>> dataiter.provide_label # Returns a list of `DataDesc`
[DataDesc[softmax_label,(3, 1L),,NCHW]]
In the above example, data is shuffled as `shuffle` parameter is set to `True`
and remaining examples are discarded as `last_batch_handle` parameter is set to `discard`.
Usage of `last_batch_handle` parameter:
>>> dataiter = mx.io.NDArrayIter(data, labels, 3, True, last_batch_handle='pad')
>>> batchidx = 0
>>> for batch in dataiter:
... batchidx += 1
...
>>> batchidx # Padding added after the examples read are over. So, 10/3+1 batches are created.
4
>>> dataiter = mx.io.NDArrayIter(data, labels, 3, True, last_batch_handle='discard')
>>> batchidx = 0
>>> for batch in dataiter:
... batchidx += 1
...
>>> batchidx # Remaining examples are discarded. So, 10/3 batches are created.
3
`NDArrayIter` also supports multiple input and labels.
>>> data = {'data1':np.zeros(shape=(10,2,2)), 'data2':np.zeros(shape=(20,2,2))}
>>> label = {'label1':np.zeros(shape=(10,1)), 'label2':np.zeros(shape=(20,1))}
>>> dataiter = mx.io.NDArrayIter(data, label, 3, True, last_batch_handle='discard')
`NDArrayIter` also supports ``mx.nd.sparse.CSRNDArray``
with `last_batch_handle` set to `discard`.
>>> csr_data = mx.nd.array(np.arange(40).reshape((10,4))).tostype('csr')
>>> labels = np.ones([10, 1])
>>> dataiter = mx.io.NDArrayIter(csr_data, labels, 3, last_batch_handle='discard')
>>> [batch.data[0] for batch in dataiter]
[
,
,
]
Parameters
----------
data: array or list of array or dict of string to array
The input data.
label: array or list of array or dict of string to array, optional
The input label.
batch_size: int
Batch size of data.
shuffle: bool, optional
Whether to shuffle the data.
Only supported if no h5py.Dataset inputs are used.
last_batch_handle : str, optional
How to handle the last batch. This parameter can be 'pad', 'discard' or
'roll_over'. 'roll_over' is intended for training and can cause problems
if used for prediction.
data_name : str, optional
The data name.
label_name : str, optional
The label name.
"""
def __init__(self, data, label=None, batch_size=1, shuffle=False,
last_batch_handle='pad', data_name='data',
label_name='softmax_label'):
super(NDArrayIter, self).__init__(batch_size)
self.data = _init_data(data, allow_empty=False, default_name=data_name)
self.label = _init_data(label, allow_empty=True, default_name=label_name)
if ((_has_instance(self.data, CSRNDArray) or _has_instance(self.label, CSRNDArray)) and
(last_batch_handle != 'discard')):
raise NotImplementedError("`NDArrayIter` only supports ``CSRNDArray``" \
" with `last_batch_handle` set to `discard`.")
# shuffle data
if shuffle:
tmp_idx = arange(self.data[0][1].shape[0], dtype=np.int32)
self.idx = random_shuffle(tmp_idx, out=tmp_idx).asnumpy()
self.data = _shuffle(self.data, self.idx)
self.label = _shuffle(self.label, self.idx)
else:
self.idx = np.arange(self.data[0][1].shape[0])
# batching
if last_batch_handle == 'discard':
new_n = self.data[0][1].shape[0] - self.data[0][1].shape[0] % batch_size
self.idx = self.idx[:new_n]
self.data_list = [x[1] for x in self.data] + [x[1] for x in self.label]
self.num_source = len(self.data_list)
self.num_data = self.idx.shape[0]
assert self.num_data >= batch_size, \
"batch_size needs to be smaller than data size."
self.cursor = -batch_size
self.batch_size = batch_size
self.last_batch_handle = last_batch_handle
@property
def provide_data(self):
"""The name and shape of data provided by this iterator."""
return [
DataDesc(k, tuple([self.batch_size] + list(v.shape[1:])), v.dtype)
for k, v in self.data
]
@property
def provide_label(self):
"""The name and shape of label provided by this iterator."""
return [
DataDesc(k, tuple([self.batch_size] + list(v.shape[1:])), v.dtype)
for k, v in self.label
]
[docs] def hard_reset(self):
"""Ignore roll over data and set to start."""
self.cursor = -self.batch_size
def reset(self):
if self.last_batch_handle == 'roll_over' and self.cursor > self.num_data:
self.cursor = -self.batch_size + (self.cursor%self.num_data)%self.batch_size
else:
self.cursor = -self.batch_size
def iter_next(self):
self.cursor += self.batch_size
return self.cursor < self.num_data
def next(self):
if self.iter_next():
return DataBatch(data=self.getdata(), label=self.getlabel(), \
pad=self.getpad(), index=None)
else:
raise StopIteration
def _getdata(self, data_source):
"""Load data from underlying arrays, internal use only."""
assert(self.cursor < self.num_data), "DataIter needs reset."
if self.cursor + self.batch_size <= self.num_data:
return [
# np.ndarray or NDArray case
x[1][self.cursor:self.cursor + self.batch_size]
if isinstance(x[1], (np.ndarray, NDArray)) else
# h5py (only supports indices in increasing order)
array(x[1][sorted(self.idx[
self.cursor:self.cursor + self.batch_size])][[
list(self.idx[self.cursor:
self.cursor + self.batch_size]).index(i)
for i in sorted(self.idx[
self.cursor:self.cursor + self.batch_size])
]]) for x in data_source
]
else:
pad = self.batch_size - self.num_data + self.cursor
return [
# np.ndarray or NDArray case
concatenate([x[1][self.cursor:], x[1][:pad]])
if isinstance(x[1], (np.ndarray, NDArray)) else
# h5py (only supports indices in increasing order)
concatenate([
array(x[1][sorted(self.idx[self.cursor:])][[
list(self.idx[self.cursor:]).index(i)
for i in sorted(self.idx[self.cursor:])
]]),
array(x[1][sorted(self.idx[:pad])][[
list(self.idx[:pad]).index(i)
for i in sorted(self.idx[:pad])
]])
]) for x in data_source
]
def getdata(self):
return self._getdata(self.data)
def getlabel(self):
return self._getdata(self.label)
def getpad(self):
if self.last_batch_handle == 'pad' and \
self.cursor + self.batch_size > self.num_data:
return self.cursor + self.batch_size - self.num_data
else:
return 0
[docs]class MXDataIter(DataIter):
"""A python wrapper a C++ data iterator.
This iterator is the Python wrapper to all native C++ data iterators, such
as `CSVIter`, `ImageRecordIter`, `MNISTIter`, etc. When initializing
`CSVIter` for example, you will get an `MXDataIter` instance to use in your
Python code. Calls to `next`, `reset`, etc will be delegated to the
underlying C++ data iterators.
Usually you don't need to interact with `MXDataIter` directly unless you are
implementing your own data iterators in C++. To do that, please refer to
examples under the `src/io` folder.
Parameters
----------
handle : DataIterHandle, required
The handle to the underlying C++ Data Iterator.
data_name : str, optional
Data name. Default to "data".
label_name : str, optional
Label name. Default to "softmax_label".
See Also
--------
src/io : The underlying C++ data iterator implementation, e.g., `CSVIter`.
"""
def __init__(self, handle, data_name='data', label_name='softmax_label', **_):
super(MXDataIter, self).__init__()
self.handle = handle
# debug option, used to test the speed with io effect eliminated
self._debug_skip_load = False
# load the first batch to get shape information
self.first_batch = None
self.first_batch = self.next()
data = self.first_batch.data[0]
label = self.first_batch.label[0]
# properties
self.provide_data = [DataDesc(data_name, data.shape, data.dtype)]
self.provide_label = [DataDesc(label_name, label.shape, label.dtype)]
self.batch_size = data.shape[0]
def __del__(self):
check_call(_LIB.MXDataIterFree(self.handle))
def debug_skip_load(self):
# Set the iterator to simply return always first batch. This can be used
# to test the speed of network without taking the loading delay into
# account.
self._debug_skip_load = True
logging.info('Set debug_skip_load to be true, will simply return first batch')
def reset(self):
self._debug_at_begin = True
self.first_batch = None
check_call(_LIB.MXDataIterBeforeFirst(self.handle))
def next(self):
if self._debug_skip_load and not self._debug_at_begin:
return DataBatch(data=[self.getdata()], label=[self.getlabel()], pad=self.getpad(),
index=self.getindex())
if self.first_batch is not None:
batch = self.first_batch
self.first_batch = None
return batch
self._debug_at_begin = False
next_res = ctypes.c_int(0)
check_call(_LIB.MXDataIterNext(self.handle, ctypes.byref(next_res)))
if next_res.value:
return DataBatch(data=[self.getdata()], label=[self.getlabel()], pad=self.getpad(),
index=self.getindex())
else:
raise StopIteration
def iter_next(self):
if self.first_batch is not None:
return True
next_res = ctypes.c_int(0)
check_call(_LIB.MXDataIterNext(self.handle, ctypes.byref(next_res)))
return next_res.value
def getdata(self):
hdl = NDArrayHandle()
check_call(_LIB.MXDataIterGetData(self.handle, ctypes.byref(hdl)))
return _ndarray_cls(hdl, False)
def getlabel(self):
hdl = NDArrayHandle()
check_call(_LIB.MXDataIterGetLabel(self.handle, ctypes.byref(hdl)))
return _ndarray_cls(hdl, False)
def getindex(self):
index_size = ctypes.c_uint64(0)
index_data = ctypes.POINTER(ctypes.c_uint64)()
check_call(_LIB.MXDataIterGetIndex(self.handle,
ctypes.byref(index_data),
ctypes.byref(index_size)))
if index_size.value:
address = ctypes.addressof(index_data.contents)
dbuffer = (ctypes.c_uint64* index_size.value).from_address(address)
np_index = np.frombuffer(dbuffer, dtype=np.uint64)
return np_index.copy()
else:
return None
def getpad(self):
pad = ctypes.c_int(0)
check_call(_LIB.MXDataIterGetPadNum(self.handle, ctypes.byref(pad)))
return pad.value
def _make_io_iterator(handle):
"""Create an io iterator by handle."""
name = ctypes.c_char_p()
desc = ctypes.c_char_p()
num_args = mx_uint()
arg_names = ctypes.POINTER(ctypes.c_char_p)()
arg_types = ctypes.POINTER(ctypes.c_char_p)()
arg_descs = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXDataIterGetIterInfo( \
handle, ctypes.byref(name), ctypes.byref(desc), \
ctypes.byref(num_args), \
ctypes.byref(arg_names), \
ctypes.byref(arg_types), \
ctypes.byref(arg_descs)))
iter_name = py_str(name.value)
narg = int(num_args.value)
param_str = _build_param_doc(
[py_str(arg_names[i]) for i in range(narg)],
[py_str(arg_types[i]) for i in range(narg)],
[py_str(arg_descs[i]) for i in range(narg)])
doc_str = ('%s\n\n' +
'%s\n' +
'Returns\n' +
'-------\n' +
'MXDataIter\n'+
' The result iterator.')
doc_str = doc_str % (desc.value, param_str)
def creator(*args, **kwargs):
"""Create an iterator.
The parameters listed below can be passed in as keyword arguments.
Parameters
----------
name : string, required.
Name of the resulting data iterator.
Returns
-------
dataiter: Dataiter
The resulting data iterator.
"""
param_keys = []
param_vals = []
for k, val in kwargs.items():
param_keys.append(k)
param_vals.append(str(val))
# create atomic symbol
param_keys = c_str_array(param_keys)
param_vals = c_str_array(param_vals)
iter_handle = DataIterHandle()
check_call(_LIB.MXDataIterCreateIter(
handle,
mx_uint(len(param_keys)),
param_keys, param_vals,
ctypes.byref(iter_handle)))
if len(args):
raise TypeError('%s can only accept keyword arguments' % iter_name)
return MXDataIter(iter_handle, **kwargs)
creator.__name__ = iter_name
creator.__doc__ = doc_str
return creator
def _init_io_module():
"""List and add all the data iterators to current module."""
plist = ctypes.POINTER(ctypes.c_void_p)()
size = ctypes.c_uint()
check_call(_LIB.MXListDataIters(ctypes.byref(size), ctypes.byref(plist)))
module_obj = sys.modules[__name__]
for i in range(size.value):
hdl = ctypes.c_void_p(plist[i])
dataiter = _make_io_iterator(hdl)
setattr(module_obj, dataiter.__name__, dataiter)
_init_io_module()
