def test_sequence(self):
test_dataset = ZipDataset(
datasets=[Dataset_(5), Dataset_(5),
Dataset_(5)], transform=None)
subset = test_dataset[[1, 3, 4]]
self.assertEqual(subset[-1], (4, 4, 4))
self.assertEqual(len(subset), 3)
def test_slicing(self):
test_dataset = ZipDataset(
datasets=[Dataset_(5), Dataset_(5),
Dataset_(5)], transform=None)
subset = test_dataset[0:2]
self.assertEqual(subset[-1], (1, 1, 1))
self.assertEqual(len(subset), 2)
def __init__(
self,
inputZ01: Sequence,
inputZ02: Sequence,
inputZ03: Sequence,
inputZ04: Sequence,
inputZ05: Sequence,
inputZ06: Sequence,
inputZ07: Sequence,
targetC01: Sequence,
targetC02: Sequence,
targetC03: Sequence,
roi_size: int,
n_samples: int,
inputZ01_transform: Optional[Callable] = None,
inputZ02_transform: Optional[Callable] = None,
inputZ03_transform: Optional[Callable] = None,
inputZ04_transform: Optional[Callable] = None,
inputZ05_transform: Optional[Callable] = None,
inputZ06_transform: Optional[Callable] = None,
inputZ07_transform: Optional[Callable] = None,
targetC01_transform: Optional[Callable] = None,
targetC02_transform: Optional[Callable] = None,
targetC03_transform: Optional[Callable] = None,
) -> None:
"""
Initializes the dataset with the filename lists. The transform `img_transform` is applied
to the images and `seg_transform` to the segmentations.
"""
items = [(inputZ01, inputZ01_transform), (inputZ02, inputZ02_transform), (inputZ03, inputZ03_transform),
(inputZ04, inputZ04_transform), (inputZ05, inputZ05_transform), (inputZ06, inputZ06_transform),
(inputZ07, inputZ07_transform),
(targetC01, targetC01_transform), (targetC02, targetC02_transform), (targetC03, targetC03_transform)]
self.roi_size = roi_size
self.n_samples = n_samples
self.set_random_state(seed=get_seed())
sampler = RandSpatialCropSamples(roi_size=(self.roi_size, self.roi_size),
num_samples=self.n_samples,
random_center=True,
random_size=False)
datasets = [PatchDataset(dataset=x[0],
patch_func=sampler,
samples_per_image=self.n_samples,
transform=x[1]) for x in items if x[0] is not None]
self.dataset = datasets[0] if len(datasets) == 1 else ZipDataset(datasets)
self._seed = 0 # transform synchronization seed
def __init__(self,
inputZ01: Sequence,
inputZ02: Sequence,
inputZ03: Sequence,
inputZ04: Sequence,
inputZ05: Sequence,
inputZ06: Sequence,
inputZ07: Sequence,
targetC01: Sequence,
targetC02: Sequence,
targetC03: Sequence,
inputZ01_transform: Optional[Callable] = None,
inputZ02_transform: Optional[Callable] = None,
inputZ03_transform: Optional[Callable] = None,
inputZ04_transform: Optional[Callable] = None,
inputZ05_transform: Optional[Callable] = None,
inputZ06_transform: Optional[Callable] = None,
inputZ07_transform: Optional[Callable] = None,
targetC01_transform: Optional[Callable] = None,
targetC02_transform: Optional[Callable] = None,
targetC03_transform: Optional[Callable] = None) -> None:
"""
Initializes the dataset with the filename lists. The transform `img_transform` is applied
to the images and `seg_transform` to the segmentations.
"""
items = [
(inputZ01, inputZ01_transform), (inputZ02, inputZ02_transform),
(inputZ03, inputZ03_transform), (inputZ04, inputZ04_transform),
(inputZ05, inputZ05_transform), (inputZ06, inputZ06_transform),
(inputZ07, inputZ07_transform), (targetC01, targetC01_transform),
(targetC02, targetC02_transform), (targetC03, targetC03_transform)
]
self.set_random_state(seed=get_seed())
datasets = [Dataset(x[0], x[1]) for x in items if x[0] is not None]
self.dataset = datasets[0] if len(datasets) == 1 else ZipDataset(
datasets)
self._seed = 0 # transform synchronization seed
def test_value(self, datasets, transform, expected_output,
expected_length):
test_dataset = ZipDataset(datasets=datasets, transform=transform)
self.assertEqual(test_dataset[0], expected_output)
self.assertEqual(len(test_dataset), expected_length)