def setUp(self):
super().setUp()
self.subjects_list = create_dummy_dataset(
num_images=10,
size_range=(10, 20),
directory=self.dir,
suffix='.nii',
force=False,
)
def setUp(self):
"""Set up test fixtures, if any."""
self.dir = Path(tempfile.gettempdir()) / 'torchio'
self.subjects_list = create_dummy_dataset(
num_images=10,
size_range=(10, 20),
directory=self.dir,
suffix='.nii',
force=False,
)
# Define training and patches sampling parameters
num_epochs = 4
patch_size = 128
queue_length = 100
samples_per_volume = 10
batch_size = 4
def model(batch, sleep_time=0.1):
"""Dummy function to simulate a forward pass through the network"""
time.sleep(sleep_time)
return batch
# Create a dummy dataset in the temporary directory, for this example
subjects_paths = create_dummy_dataset(
num_images=100,
size_range=(193, 229),
force=False,
)
# Each element of subjects_paths is a dictionary:
# subject = {
# 'one_image': dict(path=path_to_one_image, type=torchio.INTENSITY),
# 'another_image': dict(path=path_to_another_image, type=torchio.INTENSITY),
# 'a_label': dict(path=path_to_a_label, type=torchio.LABEL),
# }
# Define transforms for data normalization and augmentation
transforms = (
ZNormalization(),
RandomNoise(std_range=(0, 0.25)),
RandomAffine(scales=(0.9, 1.1), degrees=10),
