def test_save_subject(self):
dataset = SubjectsDataset(self.subjects_list, transform=lambda x: x)
_ = len(dataset) # for coverage
subject = dataset[0]
output_path = self.dir / 'test.nii.gz'
paths_dict = {'t1': output_path}
with self.assertWarns(DeprecationWarning):
dataset.save_sample(subject, paths_dict)
nii = nib.load(str(output_path))
ndims_output = len(nii.shape)
ndims_subject = len(subject['t1'].shape)
assert ndims_subject == ndims_output + 1
def test_data_loader(self):
from torch.utils.data import DataLoader
subj_list = [torchio.datasets.Colin27()]
dataset = SubjectsDataset(subj_list)
loader = DataLoader(dataset, batch_size=1, shuffle=True)
for batch in loader:
batch['t1'][DATA]
batch['brain'][DATA]
def test_data_loader(self):
from torch.utils.data import DataLoader
subj_list = [self.sample_subject]
dataset = SubjectsDataset(subj_list)
loader = DataLoader(dataset, batch_size=1, shuffle=True)
for batch in loader:
batch['t1'][DATA]
batch['label'][DATA]
def setUp(self):
super().setUp()
self.subjects = [
Subject(
image=ScalarImage(self.get_image_path(f'hs_image_{i}')),
label=LabelMap(self.get_image_path(f'hs_label_{i}')),
)
for i in range(5)
]
self.dataset = SubjectsDataset(self.subjects)
def split_dataset(dataset: Dataset, lengths: List[int]) -> List[Dataset]:
div_points = [0] + np.cumsum(lengths).tolist()
datasets = []
for i in range(len(div_points) - 1):
st = div_points[i]
end = div_points[i + 1]
datasets.append(SubjectsDataset(dataset._subjects[st:end]))
return datasets
def main():
# Define training and patches sampling parameters
num_epochs = 20
patch_size = 128
queue_length = 100
patches_per_volume = 5
batch_size = 2
# Populate a list with images
one_subject = Subject(
T1=ScalarImage('../BRATS2018_crop_renamed/LGG75_T1.nii.gz'),
T2=ScalarImage('../BRATS2018_crop_renamed/LGG75_T2.nii.gz'),
label=LabelMap('../BRATS2018_crop_renamed/LGG75_Label.nii.gz'),
)
# This subject doesn't have a T2 MRI!
another_subject = Subject(
T1=ScalarImage('../BRATS2018_crop_renamed/LGG74_T1.nii.gz'),
label=LabelMap('../BRATS2018_crop_renamed/LGG74_Label.nii.gz'),
)
subjects = [
one_subject,
another_subject,
]
subjects_dataset = SubjectsDataset(subjects)
queue_dataset = Queue(
subjects_dataset,
queue_length,
patches_per_volume,
UniformSampler(patch_size),
)
# This collate_fn is needed in the case of missing modalities
# In this case, the batch will be composed by a *list* of samples instead
# of the typical Python dictionary that is collated by default in Pytorch
batch_loader = DataLoader(
queue_dataset,
batch_size=batch_size,
collate_fn=lambda x: x,
)
# Mock PyTorch model
model = nn.Identity()
for epoch_index in range(num_epochs):
logging.info(f'Epoch {epoch_index}')
for batch in batch_loader: # batch is a *list* here, not a dictionary
logits = model(batch)
logging.info([batch[idx].keys() for idx in range(batch_size)])
logging.info(logits.shape)
logging.info('')
def setUp(self):
super().setUp()
subjects = []
for i in range(5):
image = ScalarImage(self.get_image_path(f'hs_image_{i}'))
label_path = self.get_image_path(f'hs_label_{i}',
binary=True,
force_binary_foreground=True)
label = LabelMap(label_path)
subject = Subject(image=image, label=label)
subjects.append(subject)
self.subjects = subjects
self.dataset = SubjectsDataset(self.subjects)
def setUp(self):
super().setUp()
self.subjects = [
Subject(
image=ScalarImage(self.get_image_path(f'hs_image_{i}')),
label=LabelMap(
self.get_image_path(
f'hs_label_{i}',
binary=True,
force_binary_foreground=True,
), ),
) for i in range(5)
]
self.dataset = SubjectsDataset(self.subjects)
def run_queue(self, num_workers, **kwargs):
subjects_dataset = SubjectsDataset(self.subjects_list)
patch_size = 10
sampler = UniformSampler(patch_size)
queue_dataset = Queue(
subjects_dataset,
max_length=6,
samples_per_volume=2,
sampler=sampler,
**kwargs,
)
_ = str(queue_dataset)
batch_loader = DataLoader(queue_dataset, batch_size=4)
for batch in batch_loader:
_ = batch['one_modality'][DATA]
_ = batch['segmentation'][DATA]
def setUp(self):
"""Set up test fixtures, if any."""
self.dir = Path(tempfile.gettempdir()) / '.torchio_tests'
self.dir.mkdir(exist_ok=True)
random.seed(42)
np.random.seed(42)
registration_matrix = np.array([
[1, 0, 0, 10],
[0, 1, 0, 0],
[0, 0, 1.2, 0],
[0, 0, 0, 1]
])
subject_a = Subject(
t1=ScalarImage(self.get_image_path('t1_a')),
)
subject_b = Subject(
t1=ScalarImage(self.get_image_path('t1_b')),
label=LabelMap(self.get_image_path('label_b', binary=True)),
)
subject_c = Subject(
label=LabelMap(self.get_image_path('label_c', binary=True)),
)
subject_d = Subject(
t1=ScalarImage(
self.get_image_path('t1_d'),
pre_affine=registration_matrix,
),
t2=ScalarImage(self.get_image_path('t2_d')),
label=LabelMap(self.get_image_path('label_d', binary=True)),
)
subject_a4 = Subject(
t1=ScalarImage(self.get_image_path('t1_a'), components=2),
)
self.subjects_list = [
subject_a,
subject_a4,
subject_b,
subject_c,
subject_d,
]
self.dataset = SubjectsDataset(self.subjects_list)
self.sample = self.dataset[-1] # subject_d
def iterate_dataset(subjects_list):
dataset = SubjectsDataset(subjects_list)
for _ in dataset:
pass
def test_wrong_transform_init(self):
with self.assertRaises(ValueError):
SubjectsDataset(
self.subjects_list,
transform={},
)
#
# print(1)
training_transform = tio.Compose(
[
tio.ToCanonical(),
tio.RandomBlur(std=(0, 1), seed=seed, p=0.1), # blur 50% of times
tio.RandomNoise(std=5, seed=1, p=0.5), # Gaussian noise 50% of times
tio.OneOf(
{ # either
tio.RandomAffine(scales=(0.95, 1.05), degrees=5, seed=seed):
0.75, # random affine
tio.RandomElasticDeformation(max_displacement=(5, 5, 5),
seed=seed):
0.25, # or random elastic deformation
},
p=0.8), # applied to 80% of images
])
for one_subject in dataset:
image0 = one_subject.mri
plt.imshow(image0.data[0, int(image0.shape[1] / 2), :, :])
plt.show()
break
dataset_augmented = SubjectsDataset(subjects, transform=training_transform)
for one_subject in dataset_augmented:
image = one_subject.mri
plt.imshow(image.data[0, int(image.shape[1] / 2), :, :])
plt.show()
pass
#plt.ioff()
data_ref, aff = read_image('/data/romain/data_exemple/suj_150423/mT1w_1mm.nii')
res, res_fitpar, extra_info = pd.DataFrame(), pd.DataFrame(), dict()
disp_str = disp_str_list[0]; s = 2; xx = 100
for disp_str in disp_str_list:
for s in [2, 20]: #[1, 2, 3, 5, 7, 10, 12 , 15, 20 ] : # [2,4,6] : #[1, 3 , 5 , 8, 10 , 12, 15, 20 , 25 ]:
for xx in x0:
dico_params['displacement_shift_strategy'] = disp_str
fp = corrupt_data(xx, sigma=s, method=mvt_type, amplitude=10, mvt_axes=mvt_axes)
dico_params['fitpars'] = fp
dico_params['nT'] = fp.shape[1]
t = RandomMotionFromTimeCourse(**dico_params)
if 'synth' in suj_type:
dataset = SubjectsDataset(suj, transform= torchio.Compose([tlab, t ]))
else:
dataset = SubjectsDataset(suj, transform= t )
sample = dataset[0]
fout = out_path + '/{}_{}_{}_s{}_freq{}_{}'.format(suj_type, mvt_axe_str, mvt_type, s, xx, disp_str)
fit_pars = t.fitpars - np.tile(t.to_substract[..., np.newaxis],(1,200))
# fig = plt.figure();plt.plot(fit_pars.T);plt.savefig(fout+'.png');plt.close(fig)
#sample['image'].save(fout+'.nii')
extra_info['x0'], extra_info['mvt_type'], extra_info['mvt_axe']= xx, mvt_type, mvt_axe_str
extra_info['shift_type'], extra_info['sigma'], extra_info['amp'] = disp_str, s, 10
extra_info['disp'] = np.sum(t.to_substract)
dff = pd.DataFrame(fit_pars.T); dff.columns = ['x', 'trans_y', 'z', 'r1', 'r2', 'r3']; dff['nbt'] = range(0,200)
for k,v in extra_info.items():
def test_indexing_nonint(self):
dset = SubjectsDataset(self.subjects_list)
dset[torch.tensor(0)]
