chainer.datasets.LabeledZippedImageDataset¶
-
class
chainer.datasets.
LabeledZippedImageDataset
(zipfilename, labelfilename, dtype=None, label_dtype=<class 'numpy.int32'>)[source]¶ Dataset of zipped image and label pairs.
This dataset is zip version of
LabeledImageDataset
. It takes a zipfile likeZippedImageDataset
. The label file shall contain lines like text file used inLabeledImageDataset
, but a filename in each line of the label file shall match with a file in the zip archive.Parameters: - zipfilename (str) – Path to a zipfile with images
- labelfilename (str) – Path to a label file.
i
-th line shall contain a filename and an integer label that corresponds to thei
-th sample. A filename in the label file shall match with a filename in the zip file given with zipfilename. - dtype – Data type of resulting image arrays.
chainer.config.dtype
is used by default (see Configuring Chainer). - label_dtype – Data type of the labels.
Methods
-
__getitem__
(index)[source]¶ Returns an example or a sequence of examples.
It implements the standard Python indexing and one-dimensional integer array indexing. It uses the
get_example()
method by default, but it may be overridden by the implementation to, for example, improve the slicing performance.Parameters: index (int, slice, list or numpy.ndarray) – An index of an example or indexes of examples. Returns: If index is int, returns an example created by get_example. If index is either slice or one-dimensional list or numpy.ndarray, returns a list of examples created by get_example. Example
>>> import numpy >>> from chainer import dataset >>> class SimpleDataset(dataset.DatasetMixin): ... def __init__(self, values): ... self.values = values ... def __len__(self): ... return len(self.values) ... def get_example(self, i): ... return self.values[i] ... >>> ds = SimpleDataset([0, 1, 2, 3, 4, 5]) >>> ds[1] # Access by int 1 >>> ds[1:3] # Access by slice [1, 2] >>> ds[[4, 0]] # Access by one-dimensional integer list [4, 0] >>> index = numpy.arange(3) >>> ds[index] # Access by one-dimensional integer numpy.ndarray [0, 1, 2]
-
get_example
(i)[source]¶ Returns the i-th example.
Implementations should override it. It should raise
IndexError
if the index is invalid.Parameters: i (int) – The index of the example. Returns: The i-th example.