dival.datasets.ellipses_dataset module¶
Provides EllipsesDataset.
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class
dival.datasets.ellipses_dataset.
EllipsesDataset
(image_size=128, min_pt=None, max_pt=None, train_len=32000, validation_len=3200, test_len=3200, fixed_seeds=False)[source]¶ Bases:
dival.datasets.dataset.GroundTruthDataset
Dataset with images of multiple random ellipses.
This dataset uses
odl.phantom.ellipsoid_phantom()
to create the images. The images are normalized to have a value range of[0., 1.]
with a background value of0.
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space
¶ odl.uniform_discr(min_pt, max_pt, (image_size, image_size), dtype='float32')
, with the parameters passed to__init__()
.
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shape
¶ (image_size, image_size)
, with image_size parameter passed to__init__()
. Default(128, 128)
.
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train_len
¶ train_len parameter passed to
__init__()
. Default32000
.
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validation_len
¶ validation_len parameter passed to
__init__()
. Default3200
.
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test_len
¶ test_len parameter passed to
__init__()
. Default3200
.
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random_access
¶ False
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num_elements_per_sample
¶ 1
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__init__
(image_size=128, min_pt=None, max_pt=None, train_len=32000, validation_len=3200, test_len=3200, fixed_seeds=False)[source]¶ - Parameters
image_size (int, optional) – Number of pixels per image dimension. Default:
128
.min_pt ([int, int], optional) – Minimum values of the lp space. Default:
[-image_size/2, -image_size/2]
.max_pt ([int, int], optional) – Maximum values of the lp space. Default:
[image_size/2, image_size/2]
.train_len (int or None, optional) – Length of training set. Default:
32000
. If None, infinitely many samples could be generated.validation_len (int, optional) – Length of training set. Default:
3200
.test_len (int, optional) – Length of test set. Default:
3200
.fixed_seeds (dict or bool, optional) – Seeds to use for random generation. The values of the keys
'train'
,'validation'
and'test'
are used. If a seed is None or omitted, it is choosen randomly. IfTrue
is passed, the seedsfixed_seeds={'train': 42, 'validation': 2, 'test': 1}
are used. IfFalse
is passed (the default), all seeds are chosen randomly.
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