def transform(self):
if hp.mode == '3d':
if hp.aug:
training_transform = Compose([
# ToCanonical(),
CropOrPad((hp.crop_or_pad_size), padding_mode='reflect'),
# RandomMotion(),
RandomBiasField(),
ZNormalization(),
RandomNoise(),
RandomFlip(axes=(0, )),
OneOf({
RandomAffine(): 0.8,
RandomElasticDeformation(): 0.2,
}),
])
else:
training_transform = Compose([
CropOrPad((hp.crop_or_pad_size, hp.crop_or_pad_size,
hp.crop_or_pad_size),
padding_mode='reflect'),
ZNormalization(),
])
elif hp.mode == '2d':
if hp.aug:
training_transform = Compose([
CropOrPad((hp.crop_or_pad_size), padding_mode='reflect'),
# RandomMotion(),
RandomBiasField(),
ZNormalization(),
RandomNoise(),
RandomFlip(axes=(0, )),
OneOf({
RandomAffine(): 0.8,
RandomElasticDeformation(): 0.2,
}),
])
else:
training_transform = Compose([
CropOrPad((hp.crop_or_pad_size, hp.crop_or_pad_size,
hp.crop_or_pad_size),
padding_mode='reflect'),
ZNormalization(),
])
else:
raise Exception('no such kind of mode!')
return training_transform
def initialize_transforms_simple(p=0.8):
transforms = [
RandomFlip(axes=(0, 1, 2), flip_probability=1, p=p),
#RandomAffine(scales=(0.9, 1.1), degrees=(10), isotropic=False,
# default_pad_value='otsu', image_interpolation=Interpolation.LINEAR,
# p = p, seed=None),
# *** SLOWS DOWN DATALOADER ***
#RandomElasticDeformation(num_control_points = 7, max_displacement = 7.5,
# locked_borders = 2, image_interpolation = Interpolation.LINEAR,
# p = 0.5, seed = None),
RandomMotion(degrees=10,
translation=10,
num_transforms=2,
image_interpolation='linear',
p=p),
RandomAnisotropy(axes=(0, 1, 2), downsampling=2),
RandomBiasField(coefficients=0.5, order=3, p=p),
RandomBlur(std=(0, 2), p=p),
RandomNoise(mean=0, std=(0, 5), p=p),
RescaleIntensity((0, 255))
]
transform = tio.Compose(transforms)
return transform
def test_transforms(self):
landmarks_dict = dict(
t1=np.linspace(0, 100, 13),
t2=np.linspace(0, 100, 13),
)
random_transforms = (
RandomFlip(axes=(0, 1, 2), flip_probability=1),
RandomNoise(),
RandomBiasField(),
RandomElasticDeformation(proportion_to_augment=1),
RandomAffine(),
RandomMotion(proportion_to_augment=1),
)
intensity_transforms = (
Rescale(),
ZNormalization(),
HistogramStandardization(landmarks_dict=landmarks_dict),
)
for transform in random_transforms:
sample = self.get_sample()
transformed = transform(sample)
for transform in intensity_transforms:
sample = self.get_sample()
transformed = transform(sample)
def test_transforms(self):
landmarks_dict = dict(
t1=np.linspace(0, 100, 13),
t2=np.linspace(0, 100, 13),
)
transforms = (
CenterCropOrPad((9, 21, 30)),
ToCanonical(),
Resample((1, 1.1, 1.25)),
RandomFlip(axes=(0, 1, 2), flip_probability=1),
RandomMotion(proportion_to_augment=1),
RandomGhosting(proportion_to_augment=1, axes=(0, 1, 2)),
RandomSpike(),
RandomNoise(),
RandomBlur(),
RandomSwap(patch_size=2, num_iterations=5),
Lambda(lambda x: 1.5 * x, types_to_apply=INTENSITY),
RandomBiasField(),
Rescale((0, 1)),
ZNormalization(masking_method='label'),
HistogramStandardization(landmarks_dict=landmarks_dict),
RandomElasticDeformation(proportion_to_augment=1),
RandomAffine(),
Pad((1, 2, 3, 0, 5, 6)),
Crop((3, 2, 8, 0, 1, 4)),
)
transformed = self.get_sample()
for transform in transforms:
transformed = transform(transformed)
def __init__(self, transform1, m1, p1, transform2, m2, p2):
ranges = {
'flip': np.zeros(10),
'affine': np.linspace(0, 180, 10),
'noise': np.linspace(0, 0.5, 10),
'blur': np.arange(10),
'elasticD': np.zeros(10)
}
transforms = {
'flip': lambda magnitude, p: RandomFlip(p=p),
'affine':
lambda magnitude, p: RandomAffine(degrees=(magnitude), p=p),
'noise': lambda magnitude, p: RandomNoise(std=magnitude, p=p),
'blur': lambda magnitude, p: RandomBlur(std=magnitude, p=p),
'elasticD': lambda magnitude, p: RandomElasticDeformation(p=p)
}
self.transform1 = transforms[transform1]
self.t1_input = transform1
self.m1 = ranges[transform1][m1]
self.m1_input = m1
self.p1 = p1
self.transform2 = transforms[transform2]
self.t2_input = transform2
self.m2 = ranges[transform2][m2]
self.m2_input = m2
self.p2 = p2
self.kappa = 0.0
def _get_default_transforms(self):
io_transforms = Compose([
RandomMotion(),
RandomFlip(axes=(1, )),
RandomAffine(scales=(0.9, 1.2),
degrees=(10),
isotropic=False,
default_pad_value='otsu',
image_interpolation='bspline'),
RescaleIntensity((0, 1))
])
return io_transforms
def get_brats(
data_root='/scratch/weina/dld_data/brats2019/MICCAI_BraTS_2019_Data_Training/',
fold=1,
seed=torch.distributed.get_rank()
if torch.distributed.is_initialized() else 0,
**kwargs):
""" data iter for brats
"""
logging.debug("BratsIter:: fold = {}, seed = {}".format(fold, seed))
# args for transforms
d_size, h_size, w_size = 155, 240, 240
input_size = [7, 223, 223]
spacing = (d_size / input_size[0], h_size / input_size[1],
w_size / input_size[2])
Mean, Std, Max = read_brats_mean(fold, data_root)
normalize = transforms.Normalize(mean=Mean, std=Std)
training_transform = Compose([
# RescaleIntensity((0, 1)), # so that there are no negative values for RandomMotion
# RandomMotion(),
# HistogramStandardization({MRI: landmarks}),
RandomBiasField(),
# ZNormalization(masking_method=ZNormalization.mean),
RandomNoise(),
ToCanonical(),
Resample(spacing),
# CropOrPad((48, 60, 48)),
RandomFlip(axes=(0, )),
OneOf({
RandomAffine(): 0.8,
RandomElasticDeformation(): 0.2,
}),
normalize
])
val_transform = Compose([Resample(spacing), normalize])
train = BratsIter(csv_file=os.path.join(data_root, 'IDH_label',
'train_fold_{}.csv'.format(fold)),
brats_path=os.path.join(data_root, 'all'),
brats_transform=training_transform,
shuffle=True)
val = BratsIter(csv_file=os.path.join(data_root, 'IDH_label',
'val_fold_{}.csv'.format(fold)),
brats_path=os.path.join(data_root, 'all'),
brats_transform=val_transform,
shuffle=False)
return train, val
def random_augment(x):
'''Randomly augment input data.
Returns: Randomly augmented input
'''
# Data augmentations to be used
transforms_dict = {
RandomFlip(): 1,
RandomElasticDeformation(): 1,
RandomAffine(): 1,
RandomNoise(): 1,
RandomBlur(): 1
}
# Create random transform, with a p chance to apply augmentation
transform = OneOf(transforms_dict, p=0.95)
return augment(x, transform)
def predict_majority(model, x, y):
'''Augments all samples of the original data, and chooses majority predictions predicted by the model.
Usage: predict_majority(model, x_original, y_original)
'''
# Reshape arrays
x = np.reshape(x, (len(x), 40, 40, 4, 1))
y = [x - 1 for x in y]
y = to_categorical(y, 5)
# Predict majority
x_flip = augment(x.copy(), RandomFlip())
x_ed = augment(x.copy(), RandomElasticDeformation())
x_affine = augment(x.copy(), RandomAffine())
x_noise = augment(x.copy(), RandomNoise())
x_blur = augment(x.copy(), RandomBlur())
y_true = pred_list(y)
y_pred = pred_list(model.predict(x.copy()))
y_flip = pred_list(model.predict(x_flip.copy()))
y_ed = pred_list(model.predict(x_ed.copy()))
y_affine = pred_list(model.predict(x_affine.copy()))
y_noise = pred_list(model.predict(x_noise.copy()))
y_blur = pred_list(model.predict(x_blur.copy()))
y_most = []
correct = 0
print(
'\nEntry Number | Prediction (None, Flip, Elastic Deformation, Affine, Noise, Blur) | Actual'
)
for i in range(len(y_true)):
preds = [
y_pred[i], y_flip[i], y_ed[i], y_affine[i], y_noise[i], y_blur[i]
]
most = max(set(preds), key=preds.count)
y_most.append(most)
print('Entry', i, '| Predictions:', preds, '| Most Occuring:', most,
'| Correct:', y_true[i])
if most == y_true[i]:
correct += 1
print('\nTest Accuracy: ', correct / len(y_true))
print('Quadratic Weighted Kappa: ',
cohen_kappa_score(y_true, y_most, weights='quadratic'))
def training_network(landmarks, dataset, subjects):
training_transform = Compose([
ToCanonical(),
Resample(4),
CropOrPad((48, 60, 48), padding_mode='reflect'),
RandomMotion(),
HistogramStandardization({'mri': landmarks}),
RandomBiasField(),
ZNormalization(masking_method=ZNormalization.mean),
RandomNoise(),
RandomFlip(axes=(0, )),
OneOf({
RandomAffine(): 0.8,
RandomElasticDeformation(): 0.2,
}),
])
validation_transform = Compose([
ToCanonical(),
Resample(4),
CropOrPad((48, 60, 48), padding_mode='reflect'),
HistogramStandardization({'mri': landmarks}),
ZNormalization(masking_method=ZNormalization.mean),
])
training_split_ratio = 0.9
num_subjects = len(dataset)
num_training_subjects = int(training_split_ratio * num_subjects)
training_subjects = subjects[:num_training_subjects]
validation_subjects = subjects[num_training_subjects:]
training_set = tio.SubjectsDataset(training_subjects,
transform=training_transform)
validation_set = tio.SubjectsDataset(validation_subjects,
transform=validation_transform)
print('Training set:', len(training_set), 'subjects')
print('Validation set:', len(validation_set), 'subjects')
return training_set, validation_set
def flip(axes=0, p=1):
return RandomFlip(axes=axes, p=p)
def get_data_loader(cfg: DictConfig, _) -> dict:
log = logging.getLogger(__name__)
transform = Compose([
RandomMotion(),
RandomBiasField(),
RandomNoise(),
RandomFlip(axes=(0, )),
])
log.info(f"Data loader selected: {cfg['dataset']}")
try:
log.info("Attempting to use defined data loader")
dataset = getattr(datasets, cfg["dataset"])(cfg, transform)
except ImportError:
log.info(
"Not a defined data loader... Attempting to use torchio loader")
dataset = getattr(torchio.datasets,
cfg["dataset"])(root=cfg["base_path"],
transform=transform,
download=True)
for subject in random.sample(dataset._subjects, cfg["plot_number"]):
plot_subject(
subject,
os.path.join(os.environ["OUTPUT_PATH"], cfg["save_plot_dir"],
subject["subject_id"]),
)
sampler = GridSampler(patch_size=cfg["patch_size"])
samples_per_volume = len(sampler._compute_locations(
dataset[0])) # type: ignore
with open_dict(cfg):
cfg["size"] = dataset[0].spatial_shape
val_size = max(1, int(0.2 * len(dataset)))
test_set, train_set, val_set = split_dataset(
dataset, [21, len(dataset) - val_size - 21, val_size])
train_loader = __create_data_loader(
train_set,
queue_max_length=samples_per_volume * cfg["queue_length"],
queue_samples_per_volume=samples_per_volume,
sampler=sampler,
verbose=log.level > 0,
batch_size=cfg["batch"],
)
val_loader = __create_data_loader(
val_set,
queue_max_length=samples_per_volume * cfg["queue_length"],
queue_samples_per_volume=samples_per_volume,
sampler=sampler,
verbose=log.level > 0,
batch_size=cfg["batch"],
)
test_loader = __create_data_loader(
test_set,
queue_max_length=samples_per_volume * cfg["queue_length"],
queue_samples_per_volume=samples_per_volume,
sampler=sampler,
verbose=log.level > 0,
batch_size=cfg["batch"],
)
return {
"data_loader_train": train_loader,
"data_loader_val": val_loader,
"data_loader_test": test_loader,
}
def compose_transforms() -> Compose:
print(f"{ctime()}: Setting up transformations...")
"""
# Our Preprocessing Options available in TorchIO are:
* Intensity
- NormalizationTransform
- RescaleIntensity
- ZNormalization
- HistogramStandardization
* Spatial
- CropOrPad
- Crop
- Pad
- Resample
- ToCanonical
We should read and experiment with these, but for now will just use a bunch with
the default values.
"""
preprocessors = [
ToCanonical(p=1),
ZNormalization(masking_method=None,
p=1), # alternately, use RescaleIntensity
]
"""
# Our Augmentation Options available in TorchIO are:
* Spatial
- RandomFlip
- RandomAffine
- RandomElasticDeformation
* Intensity
- RandomMotion
- RandomGhosting
- RandomSpike
- RandomBiasField
- RandomBlur
- RandomNoise
- RandomSwap
We should read and experiment with these, but for now will just use a bunch with
the default values.
"""
augments = [
RandomFlip(axes=(0, 1, 2), flip_probability=0.5),
RandomAffine(image_interpolation="linear",
p=0.8), # default, compromise on speed + quality
# this will be most processing intensive, leave out for now, see results
# RandomElasticDeformation(p=1),
RandomMotion(),
RandomSpike(),
RandomBiasField(),
RandomBlur(),
RandomNoise(),
]
transform = Compose(preprocessors + augments)
print(f"{ctime()}: Transformations registered.")
return transform
verbose = True
patch_size = 128
batch_size = 4
scales = (0.75, 0.75)
degrees = (-5, -5)
axes = (0, )
transforms = (
RandomAffine(scales=scales,
degrees=degrees,
image_interpolation=Interpolation.BSPLINE,
isotropic=False,
verbose=verbose),
RandomFlip(axes, verbose=verbose),
)
transform = Compose(transforms)
subjects_dataset = ImagesDataset(subjects_paths,
transform=transform,
verbose=verbose)
sample = subjects_dataset[0]
sampler = LabelSampler(sample, patch_size)
loader = DataLoader(sampler, batch_size=batch_size)
# TODO: check that this works as expected, use dummy data
for batch in islice(loader, 1):
save_batch(batch, '/tmp/batch')
# Mock PyTorch model
model = lambda x: x
# Define training and patches sampling parameters
num_epochs = 4
patch_size = 128
queue_length = 100
samples_per_volume = 1
batch_size = 2
# Define transforms for data normalization and augmentation
transforms = (
ZNormalization(),
RandomAffine(scales=(0.9, 1.1), degrees=10),
RandomNoise(std_range=(0, 0.25)),
RandomFlip(axes=(0, )),
)
transform = Compose(transforms)
# Populate a list with dictionaries of paths
one_subject_dict = {
'T1':
dict(path='../BRATS2018_crop_renamed/LGG75_T1.nii.gz',
type=torchio.INTENSITY),
'T2':
dict(path='../BRATS2018_crop_renamed/LGG75_T2.nii.gz',
type=torchio.INTENSITY),
'label':
dict(path='../BRATS2018_crop_renamed/LGG75_Label.nii.gz',
type=torchio.LABEL),
}
fig, ax = plt.subplots(dpi=100)
plot_histogram(ax, znormed.mri.data, label='Z-normed', alpha=1)
ax.set_title('Intensity values of one sample after z-normalization')
ax.set_xlabel('Intensity')
ax.grid()
training_transform = Compose([
ToCanonical(),
# Resample(4),
CropOrPad((112, 112, 48), padding_mode=0), #reflect , original 112,112,48
RandomMotion(num_transforms=6, image_interpolation='nearest', p=0.2),
HistogramStandardization({'mri': landmarks}),
RandomBiasField(p=0.2),
RandomBlur(p=0.2),
ZNormalization(masking_method=ZNormalization.mean),
RandomFlip(axes=['inferior-superior'], flip_probability=0.2),
# RandomNoise(std=0.5, p=0.2),
RandomGhosting(intensity=1.8, p=0.2),
# RandomNoise(),
# RandomFlip(axes=(0,)),
# OneOf({
# RandomAffine(): 0.8,
# RandomElasticDeformation(): 0.2,
# }),
])
validation_transform = Compose([
ToCanonical(),
# Resample(4),
CropOrPad((112, 112, 48), padding_mode=0), #original 112,112,48
# RandomMotion(num_transforms=6, image_interpolation='nearest', p = 0.2),
def main():
opt = parsing_data()
print("[INFO]Reading data")
# Dictionary with data parameters for NiftyNet Reader
if torch.cuda.is_available():
print('[INFO] GPU available.')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else:
raise Exception(
"[INFO] No GPU found or Wrong gpu id, please run without --cuda")
# FOLDERS
fold_dir = opt.model_dir
fold_dir_model = os.path.join(fold_dir, 'models')
if not os.path.exists(fold_dir_model):
os.makedirs(fold_dir_model)
save_path = os.path.join(fold_dir_model, './CP_{}.pth')
output_path = os.path.join(fold_dir, 'output')
if not os.path.exists(output_path):
os.makedirs(output_path)
output_path = os.path.join(output_path, 'output_{}.nii.gz')
# LOGGING
orig_stdout = sys.stdout
if os.path.exists(os.path.join(fold_dir, 'out.txt')):
compt = 0
while os.path.exists(
os.path.join(fold_dir, 'out_' + str(compt) + '.txt')):
compt += 1
f = open(os.path.join(fold_dir, 'out_' + str(compt) + '.txt'), 'w')
else:
f = open(os.path.join(fold_dir, 'out.txt'), 'w')
sys.stdout = f
# SPLITS
split_path_source = opt.dataset_split_source
assert os.path.isfile(split_path_source), 'source file not found'
split_path_target = opt.dataset_split_target
assert os.path.isfile(split_path_target), 'target file not found'
split_path = dict()
split_path['source'] = split_path_source
split_path['target'] = split_path_target
path_file = dict()
path_file['source'] = opt.path_source
path_file['target'] = opt.path_target
list_split = [
'training',
'validation',
]
paths_dict = dict()
for domain in ['source', 'target']:
df_split = pd.read_csv(split_path[domain], header=None)
list_file = dict()
for split in list_split:
list_file[split] = df_split[df_split[1].isin([split])][0].tolist()
paths_dict_domain = {split: [] for split in list_split}
for split in list_split:
for subject in list_file[split]:
subject_data = []
for modality in MODALITIES[domain]:
subject_data.append(
Image(
modality,
path_file[domain] + subject + modality + '.nii.gz',
torchio.INTENSITY))
if split in ['training', 'validation']:
subject_data.append(
Image('label',
path_file[domain] + subject + 'Label.nii.gz',
torchio.LABEL))
#subject_data[] =
paths_dict_domain[split].append(Subject(*subject_data))
print(domain, split, len(paths_dict_domain[split]))
paths_dict[domain] = paths_dict_domain
# PREPROCESSING
transform_training = dict()
transform_validation = dict()
for domain in ['source', 'target']:
transform_training[domain] = (
ToCanonical(),
ZNormalization(),
CenterCropOrPad((144, 192, 48)),
RandomAffine(scales=(0.9, 1.1), degrees=10),
RandomNoise(std_range=(0, 0.10)),
RandomFlip(axes=(0, )),
)
transform_training[domain] = Compose(transform_training[domain])
transform_validation[domain] = (
ToCanonical(),
ZNormalization(),
CenterCropOrPad((144, 192, 48)),
)
transform_validation[domain] = Compose(transform_validation[domain])
transform = {
'training': transform_training,
'validation': transform_validation
}
# MODEL
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
print("[INFO] Building model")
model = Generic_UNet(input_modalities=MODALITIES_TARGET,
base_num_features=32,
num_classes=nb_classes,
num_pool=4,
num_conv_per_stage=2,
feat_map_mul_on_downscale=2,
conv_op=torch.nn.Conv3d,
norm_op=torch.nn.InstanceNorm3d,
norm_op_kwargs=norm_op_kwargs,
nonlin=net_nonlin,
nonlin_kwargs=net_nonlin_kwargs,
convolutional_pooling=False,
convolutional_upsampling=False,
final_nonlin=torch.nn.Softmax(1))
print("[INFO] Training")
train(paths_dict, model, transform, device, save_path, opt)
sys.stdout = orig_stdout
f.close()
train_subject = torchio.Subject(
data=torchio.Image(tensor=torch.from_numpy(train_data),
label=torchio.INTENSITY),
label=torchio.Image(tensor=torch.from_numpy(train_seg),
label=torchio.LABEL),
)
valid_subject = torchio.Subject(
data=torchio.Image(tensor=torch.from_numpy(valid_data),
label=torchio.INTENSITY),
label=torchio.Image(tensor=torch.from_numpy(valid_seg),
label=torchio.LABEL),
)
# Define the transforms for the set of training patches
training_transform = Compose([
RandomNoise(p=0.2),
RandomFlip(axes=(0, 1, 2)),
RandomBlur(p=0.2),
OneOf({
RandomAffine(): 0.8,
RandomElasticDeformation(): 0.2,
}, p=0.5), # Changed from p=0.75 24/6/20
])
# Create the datasets
training_dataset = torchio.ImagesDataset(
[train_subject], transform=training_transform)
validation_dataset = torchio.ImagesDataset(
[valid_subject])
# Define the queue of sampled patches for training and validation
sampler = torchio.data.UniformSampler(PATCH_SIZE)
patches_training_set = torchio.Queue(
def get_torchio_transformer(mask=False):
return RandomFlip(axes=axes,
flip_probability=flip_probability,
p=p,
seed=seed)
def flip(parameters):
return RandomFlip(axes=parameters["axis"], p=parameters["probability"])
def get_torchio_transformer(mask=False):
return RandomFlip(axes, flip_probability, p, seed, is_tensor)
def main():
opt = parsing_data()
print("[INFO] Reading data")
# Dictionary with data parameters for NiftyNet Reader
if torch.cuda.is_available():
print('[INFO] GPU available.')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else:
raise Exception(
"[INFO] No GPU found or Wrong gpu id, please run without --cuda")
# FOLDERS
fold_dir = opt.model_dir
fold_dir_model = os.path.join(fold_dir, 'models')
if not os.path.exists(fold_dir_model):
os.makedirs(fold_dir_model)
save_path = os.path.join(fold_dir_model, './CP_{}.pth')
output_path = os.path.join(fold_dir, 'output')
if not os.path.exists(output_path):
os.makedirs(output_path)
output_path = os.path.join(output_path, 'output_{}.nii.gz')
# LOGGING
orig_stdout = sys.stdout
if os.path.exists(os.path.join(fold_dir, 'out.txt')):
compt = 0
while os.path.exists(
os.path.join(fold_dir, 'out_' + str(compt) + '.txt')):
compt += 1
f = open(os.path.join(fold_dir, 'out_' + str(compt) + '.txt'), 'w')
else:
f = open(os.path.join(fold_dir, 'out.txt'), 'w')
#sys.stdout = f
print("[INFO] Hyperparameters")
print('Alpha: {}'.format(opt.alpha))
print('Beta: {}'.format(opt.beta))
print('Beta_DA: {}'.format(opt.beta_da))
print('Weight Reg: {}'.format(opt.weight_crf))
# SPLITS
split_path_source = opt.dataset_split_source
assert os.path.isfile(split_path_source), 'source file not found'
split_path_target = opt.dataset_split_target
assert os.path.isfile(split_path_target), 'target file not found'
split_path = dict()
split_path['source'] = split_path_source
split_path['target'] = split_path_target
path_file = dict()
path_file['source'] = opt.path_source
path_file['target'] = opt.path_target
list_split = ['training', 'validation', 'inference']
paths_dict = dict()
for domain in ['source', 'target']:
df_split = pd.read_csv(split_path[domain], header=None)
list_file = dict()
for split in list_split:
list_file[split] = df_split[df_split[1].isin([split])][0].tolist()
list_file['inference'] += list_file['validation']
paths_dict_domain = {split: [] for split in list_split}
for split in list_split:
for subject in list_file[split]:
subject_data = []
for modality in MODALITIES[domain]:
subject_data.append(
Image(
modality,
path_file[domain] + subject + modality + '.nii.gz',
torchio.INTENSITY))
if split in ['training', 'validation']:
if domain == 'source':
subject_data.append(
Image(
'label',
path_file[domain] + subject + 't1_seg.nii.gz',
torchio.LABEL))
else:
subject_data.append(
Image(
'scribble', path_file[domain] + subject +
't2scribble_cor.nii.gz', torchio.LABEL))
#subject_data[] =
paths_dict_domain[split].append(Subject(*subject_data))
print(domain, split, len(paths_dict_domain[split]))
paths_dict[domain] = paths_dict_domain
# PREPROCESSING
transform_training = dict()
transform_validation = dict()
for domain in ['source', 'target']:
transformations = (
ToCanonical(),
ZNormalization(),
CenterCropOrPad((288, 128, 48)),
RandomAffine(scales=(0.9, 1.1), degrees=10),
RandomNoise(std_range=(0, 0.10)),
RandomFlip(axes=(0, )),
)
transform_training[domain] = Compose(transformations)
for domain in ['source', 'target']:
transformations = (ToCanonical(), ZNormalization(),
CenterCropOrPad((288, 128, 48)))
transform_validation[domain] = Compose(transformations)
transform = {
'training': transform_training,
'validation': transform_validation
}
# MODEL
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
print("[INFO] Building model")
model = UNet2D5(input_channels=1,
base_num_features=16,
num_classes=NB_CLASSES,
num_pool=4,
conv_op=nn.Conv3d,
norm_op=nn.InstanceNorm3d,
norm_op_kwargs=norm_op_kwargs,
nonlin=net_nonlin,
nonlin_kwargs=net_nonlin_kwargs)
print("[INFO] Training")
#criterion = DC_and_CE_loss({}, {})
criterion = DC_CE(NB_CLASSES)
train(paths_dict, model, transform, criterion, device, save_path, opt)
