def main(hdf_file):
# Use a pad extractor in order to compensate for the valid convolutions of the network. Actual image information is padded
extractor = extr.PadDataExtractor(
(2, 2, 2), extr.DataExtractor(categories=(defs.KEY_IMAGES, )))
# Adapted permutation due to the additional dimension
transform = tfm.Permute(permutation=(3, 0, 1, 2),
entries=(defs.KEY_IMAGES, ))
# Creating patch indexing strategy with patch_shape that equal the network output shape
indexing_strategy = extr.PatchWiseIndexing(patch_shape=(32, 32, 32))
dataset = extr.PymiaDatasource(hdf_file, indexing_strategy, extractor,
transform)
direct_extractor = extr.ComposeExtractor([
extr.ImagePropertiesExtractor(),
extr.DataExtractor(categories=(defs.KEY_LABELS, defs.KEY_IMAGES))
])
assembler = assm.SubjectAssembler(dataset)
# torch specific handling
pytorch_dataset = pymia_torch.PytorchDatasetAdapter(dataset)
loader = torch_data.dataloader.DataLoader(pytorch_dataset,
batch_size=2,
shuffle=False)
# dummy CNN with valid convolutions instead of same convolutions
dummy_network = nn.Sequential(
nn.Conv3d(in_channels=2, out_channels=8, kernel_size=3, padding=0),
nn.Conv3d(in_channels=8, out_channels=1, kernel_size=3, padding=0),
nn.Sigmoid())
torch.set_grad_enabled(False)
nb_batches = len(loader)
# looping over the data in the dataset
for i, batch in enumerate(loader):
x, sample_indices = batch[defs.KEY_IMAGES], batch[
defs.KEY_SAMPLE_INDEX]
prediction = dummy_network(x)
numpy_prediction = prediction.numpy().transpose((0, 2, 3, 4, 1))
is_last = i == nb_batches - 1
assembler.add_batch(numpy_prediction, sample_indices.numpy(), is_last)
for subject_index in assembler.subjects_ready:
subject_prediction = assembler.get_assembled_subject(subject_index)
direct_sample = dataset.direct_extract(direct_extractor,
subject_index)
target, image_properties = direct_sample[
defs.KEY_LABELS], direct_sample[defs.KEY_PROPERTIES]
def main(hdf_file, is_meta):
if not is_meta:
extractor = extr.DataExtractor(categories=(defs.KEY_IMAGES, ))
else:
extractor = extr.FilesystemDataExtractor(
categories=(defs.KEY_IMAGES, ))
transform = tfm.Permute(permutation=(2, 0, 1), entries=(defs.KEY_IMAGES, ))
indexing_strategy = extr.SliceIndexing()
dataset = extr.PymiaDatasource(hdf_file, indexing_strategy, extractor,
transform)
direct_extractor = extr.ComposeExtractor([
extr.ImagePropertiesExtractor(),
extr.DataExtractor(categories=(defs.KEY_LABELS, defs.KEY_IMAGES))
])
assembler = assm.SubjectAssembler(dataset)
# torch specific handling
pytorch_dataset = pymia_torch.PytorchDatasetAdapter(dataset)
loader = torch_data.dataloader.DataLoader(pytorch_dataset,
batch_size=2,
shuffle=False)
dummy_network = nn.Sequential(
nn.Conv2d(in_channels=2, out_channels=8, kernel_size=3, padding=1),
nn.Conv2d(in_channels=8, out_channels=1, kernel_size=3, padding=1),
nn.Sigmoid())
torch.set_grad_enabled(False)
nb_batches = len(loader)
# looping over the data in the dataset
for i, batch in enumerate(loader):
x, sample_indices = batch[defs.KEY_IMAGES], batch[
defs.KEY_SAMPLE_INDEX]
prediction = dummy_network(x)
numpy_prediction = prediction.numpy().transpose((0, 2, 3, 1))
is_last = i == nb_batches - 1
assembler.add_batch(numpy_prediction, sample_indices.numpy(), is_last)
for subject_index in assembler.subjects_ready:
subject_prediction = assembler.get_assembled_subject(subject_index)
direct_sample = dataset.direct_extract(direct_extractor,
subject_index)
target, image_properties = direct_sample[
defs.KEY_LABELS], direct_sample[defs.KEY_PROPERTIES]
def main(hdf_file):
extractor = extr.DataExtractor(categories=(defs.KEY_IMAGES, ))
# no transformation needed because TensorFlow uses channel-last
transform = None
indexing_strategy = extr.SliceIndexing()
dataset = extr.PymiaDatasource(hdf_file, indexing_strategy, extractor,
transform)
direct_extractor = extr.ComposeExtractor([
extr.ImagePropertiesExtractor(),
extr.DataExtractor(categories=(defs.KEY_LABELS, defs.KEY_IMAGES))
])
assembler = assm.SubjectAssembler(dataset)
# TensorFlow specific handling
gen_fn = pymia_tf.get_tf_generator(dataset)
tf_dataset = tf.data.Dataset.from_generator(generator=gen_fn,
output_types={
defs.KEY_IMAGES:
tf.float32,
defs.KEY_SAMPLE_INDEX:
tf.int64
})
tf_dataset = tf_dataset.batch(2)
dummy_network = keras.Sequential([
layers.Conv2D(8, kernel_size=3, padding='same'),
layers.Conv2D(2, kernel_size=3, padding='same', activation='sigmoid')
])
nb_batches = len(dataset) // 2
# looping over the data in the dataset
for i, batch in enumerate(tf_dataset):
x, sample_indices = batch[defs.KEY_IMAGES], batch[
defs.KEY_SAMPLE_INDEX]
prediction = dummy_network(x)
numpy_prediction = prediction.numpy()
is_last = i == nb_batches - 1
assembler.add_batch(numpy_prediction, sample_indices.numpy(), is_last)
for subject_index in assembler.subjects_ready:
subject_prediction = assembler.get_assembled_subject(subject_index)
direct_sample = dataset.direct_extract(direct_extractor,
subject_index)
def main(hdf_file, log_dir):
# initialize the evaluator with the metrics and the labels to evaluate
metrics = [metric.DiceCoefficient()]
labels = {1: 'WHITEMATTER',
2: 'GREYMATTER',
3: 'HIPPOCAMPUS',
4: 'AMYGDALA',
5: 'THALAMUS'}
evaluator = eval_.SegmentationEvaluator(metrics, labels)
# we want to log the mean and standard deviation of the metrics among all subjects of the dataset
functions = {'MEAN': np.mean, 'STD': np.std}
statistics_aggregator = writer.StatisticsAggregator(functions=functions)
console_writer = writer.ConsoleStatisticsWriter(functions=functions)
# initialize TensorBoard writer
tb = tensorboard.SummaryWriter(os.path.join(log_dir, 'logging-example-torch'))
# setup the training datasource
train_subjects, valid_subjects = ['Subject_1', 'Subject_2', 'Subject_3'], ['Subject_4']
extractor = extr.DataExtractor(categories=(defs.KEY_IMAGES, defs.KEY_LABELS))
indexing_strategy = extr.SliceIndexing()
augmentation_transforms = [augm.RandomElasticDeformation(), augm.RandomMirror()]
transforms = [tfm.Permute(permutation=(2, 0, 1)), tfm.Squeeze(entries=(defs.KEY_LABELS,))]
train_transforms = tfm.ComposeTransform(augmentation_transforms + transforms)
train_dataset = extr.PymiaDatasource(hdf_file, indexing_strategy, extractor, train_transforms,
subject_subset=train_subjects)
# setup the validation datasource
valid_transforms = tfm.ComposeTransform([tfm.Permute(permutation=(2, 0, 1))])
valid_dataset = extr.PymiaDatasource(hdf_file, indexing_strategy, extractor, valid_transforms,
subject_subset=valid_subjects)
direct_extractor = extr.ComposeExtractor(
[extr.SubjectExtractor(),
extr.ImagePropertiesExtractor(),
extr.DataExtractor(categories=(defs.KEY_LABELS,))]
)
assembler = assm.SubjectAssembler(valid_dataset)
# torch specific handling
pytorch_train_dataset = pymia_torch.PytorchDatasetAdapter(train_dataset)
train_loader = torch_data.dataloader.DataLoader(pytorch_train_dataset, batch_size=16, shuffle=True)
pytorch_valid_dataset = pymia_torch.PytorchDatasetAdapter(valid_dataset)
valid_loader = torch_data.dataloader.DataLoader(pytorch_valid_dataset, batch_size=16, shuffle=False)
u_net = unet.UNetModel(ch_in=2, ch_out=6, n_channels=16, n_pooling=3).to(device)
print(u_net)
optimizer = optim.Adam(u_net.parameters(), lr=1e-3)
train_batches = len(train_loader)
# looping over the data in the dataset
epochs = 100
for epoch in range(epochs):
u_net.train()
print(f'Epoch {epoch + 1}/{epochs}')
# training
print('training')
for i, batch in enumerate(train_loader):
x, y = batch[defs.KEY_IMAGES].to(device), batch[defs.KEY_LABELS].to(device).long()
logits = u_net(x)
optimizer.zero_grad()
loss = F.cross_entropy(logits, y)
loss.backward()
optimizer.step()
tb.add_scalar('train/loss', loss.item(), epoch*train_batches + i)
print(f'[{i + 1}/{train_batches}]\tloss: {loss.item()}')
# validation
print('validation')
with torch.no_grad():
u_net.eval()
valid_batches = len(valid_loader)
for i, batch in enumerate(valid_loader):
x, sample_indices = batch[defs.KEY_IMAGES].to(device), batch[defs.KEY_SAMPLE_INDEX]
logits = u_net(x)
prediction = logits.argmax(dim=1, keepdim=True)
numpy_prediction = prediction.cpu().numpy().transpose((0, 2, 3, 1))
is_last = i == valid_batches - 1
assembler.add_batch(numpy_prediction, sample_indices.numpy(), is_last)
for subject_index in assembler.subjects_ready:
subject_prediction = assembler.get_assembled_subject(subject_index)
direct_sample = train_dataset.direct_extract(direct_extractor, subject_index)
target, image_properties = direct_sample[defs.KEY_LABELS], direct_sample[defs.KEY_PROPERTIES]
# evaluate the prediction against the reference
evaluator.evaluate(subject_prediction[..., 0], target[..., 0], direct_sample[defs.KEY_SUBJECT])
# calculate mean and standard deviation of each metric
results = statistics_aggregator.calculate(evaluator.results)
# log to TensorBoard into category train
for result in results:
tb.add_scalar(f'valid/{result.metric}-{result.id_}', result.value, epoch)
console_writer.write(evaluator.results)
# clear results such that the evaluator is ready for the next evaluation
evaluator.clear()
def __init__(self,
config: cfg.Configuration,
subjects_train,
subjects_valid,
subjects_test,
collate_fn=pymia_cnv.TensorFlowCollate()):
super().__init__()
indexing_strategy = PointCloudIndexing(config.no_points)
self.dataset = pymia_extr.ParameterizableDataset(
config.database_file,
indexing_strategy,
pymia_extr.SubjectExtractor(), # for the usual select_indices
None)
self.no_subjects_train = len(subjects_train)
self.no_subjects_valid = len(subjects_valid)
self.no_subjects_test = len(
subjects_valid
) # same as validation for this kind of cross validation
# get sampler ids by subjects
sampler_ids_train = pymia_extr.select_indices(
self.dataset, pymia_extr.SubjectSelection(subjects_train))
sampler_ids_valid = pymia_extr.select_indices(
self.dataset, pymia_extr.SubjectSelection(subjects_valid))
categories = ('images', 'labels')
categories_tfm = ('images', 'labels')
if config.use_image_information:
image_information_categories = (data.KEY_IMAGE_INFORMATION, )
categories += image_information_categories
categories_tfm += image_information_categories
collate_fn.entries += image_information_categories
# define point cloud shuffler for augmentation
sizes = {}
for idx in range(len(self.dataset.get_subjects())):
sample = self.dataset.direct_extract(PointCloudSizeExtractor(),
idx)
sizes[idx] = sample['size']
self.point_cloud_shuffler = aug.PointCloudShuffler(sizes)
self.point_cloud_shuffler_valid = aug.PointCloudShuffler(
sizes
) # will only shuffle once at instantiation because shuffle() is not called during training (see set_seed)
data_extractor_train = aug.ShuffledDataExtractor(
self.point_cloud_shuffler, categories)
data_extractor_valid = aug.ShuffledDataExtractor(
self.point_cloud_shuffler_valid, categories)
data_extractor_test = aug.ShuffledDataExtractor(
self.point_cloud_shuffler_valid, categories=('indices', 'labels'))
# define extractors
self.extractor_train = pymia_extr.ComposeExtractor([
pymia_extr.NamesExtractor(), # required for SelectiveDataExtractor
pymia_extr.SubjectExtractor(), # required for plotting
PointCloudSizeExtractor(), # to init_shape in SubjectAssembler
data_extractor_train,
pymia_extr.IndexingExtractor(
), # for SubjectAssembler (assembling)
pymia_extr.ImageShapeExtractor() # for SubjectAssembler (shape)
])
self.extractor_valid = pymia_extr.ComposeExtractor([
pymia_extr.NamesExtractor(), # required for SelectiveDataExtractor
pymia_extr.SubjectExtractor(), # required for plotting
PointCloudSizeExtractor(), # to init_shape in SubjectAssembler
data_extractor_valid,
pymia_extr.IndexingExtractor(),
pymia_extr.ImageShapeExtractor()
])
self.extractor_test = pymia_extr.ComposeExtractor([
pymia_extr.NamesExtractor(), # required for SelectiveDataExtractor
pymia_extr.SubjectExtractor(),
data_extractor_test, # we need the indices, i.e. the point's coordinates,
# to convert the point cloud back to an image
pymia_extr.DataExtractor(
categories=('gt', ),
ignore_indexing=True), # the ground truth is used for the
# validation at config.save_validation_nth_epoch
pymia_extr.ImagePropertiesExtractor(),
pymia_extr.ImageShapeExtractor()
])
# define transforms for extraction
self.extraction_transform_train = pymia_tfm.ComposeTransform([
pymia_tfm.Squeeze(entries=('labels', ),
squeeze_axis=-1), # for PyTorch loss functions
pymia_tfm.LambdaTransform(
lambda_fn=lambda np_data: np_data.astype(np.int64),
entries=('labels', )),
# for PyTorch loss functions
])
self.extraction_transform_valid = pymia_tfm.ComposeTransform([
pymia_tfm.Squeeze(entries=('labels', ),
squeeze_axis=-1), # for PyTorch loss functions
pymia_tfm.LambdaTransform(
lambda_fn=lambda np_data: np_data.astype(np.int64),
entries=('labels', ))
# for PyTorch loss functions
])
if config.use_jitter:
self.extraction_transform_train.transforms.append(
aug.PointCloudJitter())
self.extraction_transform_valid.transforms.append(
aug.PointCloudJitter())
if config.use_rotation:
self.extraction_transform_train.transforms.append(
aug.PointCloudRotate())
self.extraction_transform_valid.transforms.append(
aug.PointCloudRotate())
# need to add probability concatenation after augmentation!
if config.use_point_feature:
self.extraction_transform_train.transforms.append(
ConcatenateCoordinatesAndPointFeatures())
self.extraction_transform_valid.transforms.append(
ConcatenateCoordinatesAndPointFeatures())
if config.use_image_information:
spatial_size = config.image_information_config.spatial_size
def slice_patches(np_data):
z = (np_data.shape[1] - spatial_size) // 2
y = (np_data.shape[2] - spatial_size) // 2
x = (np_data.shape[3] - spatial_size) // 2
np_data = np_data[:, z:(z + spatial_size),
y:(y + spatial_size),
x:(x + spatial_size), :]
return np_data
self.extraction_transform_train.transforms.append(
pymia_tfm.LambdaTransform(
lambda_fn=slice_patches,
entries=image_information_categories))
self.extraction_transform_valid.transforms.append(
pymia_tfm.LambdaTransform(
lambda_fn=slice_patches,
entries=image_information_categories))
# define loaders
training_sampler = pymia_extr.SubsetRandomSampler(sampler_ids_train)
self.loader_train = pymia_extr.DataLoader(self.dataset,
config.batch_size_training,
sampler=training_sampler,
collate_fn=collate_fn,
num_workers=1)
validation_sampler = pymia_extr.SubsetSequentialSampler(
sampler_ids_valid)
self.loader_valid = pymia_extr.DataLoader(self.dataset,
config.batch_size_testing,
sampler=validation_sampler,
collate_fn=collate_fn,
num_workers=1)
self.loader_test = pymia_extr.DataLoader(self.dataset,
config.batch_size_testing,
sampler=validation_sampler,
collate_fn=collate_fn,
num_workers=1)
self.extraction_transform_test = None
def main(hdf_file: str, log_dir: str):
# initialize the evaluator with the metrics and the labels to evaluate
metrics = [metric.DiceCoefficient()]
labels = {1: 'WHITEMATTER', 2: 'GREYMATTER', 5: 'THALAMUS'}
evaluator = eval_.SegmentationEvaluator(metrics, labels)
# we want to log the mean and standard deviation of the metrics among all subjects of the dataset
functions = {'MEAN': np.mean, 'STD': np.std}
statistics_aggregator = writer.StatisticsAggregator(functions=functions)
# initialize TensorBoard writer
# tb = tensorboard.SummaryWriter(os.path.join(log_dir, 'logging-example-torch'))
tb = tf.summary.create_file_writer(
os.path.join(log_dir, 'logging-example-tensorflow'))
# initialize the data handling
dataset = extr.PymiaDatasource(
hdf_file, extr.SliceIndexing(),
extr.DataExtractor(categories=(defs.KEY_IMAGES, )))
gen_fn = pymia_tf.get_tf_generator(dataset)
tf_dataset = tf.data.Dataset.from_generator(generator=gen_fn,
output_types={
defs.KEY_IMAGES:
tf.float32,
defs.KEY_SAMPLE_INDEX:
tf.int64
})
loader = tf_dataset.batch(100)
assembler = assm.SubjectAssembler(dataset)
direct_extractor = extr.ComposeExtractor([
extr.SubjectExtractor(), # extraction of the subject name
extr.ImagePropertiesExtractor(
), # Extraction of image properties (origin, spacing, etc.) for storage
extr.DataExtractor(
categories=(defs.KEY_LABELS,
)) # Extraction of "labels" entries for evaluation
])
# initialize a dummy network, which returns a random prediction
class DummyNetwork(tf.keras.Model):
def call(self, inputs):
return tf.random.uniform((*inputs.shape[:-1], 1),
0,
6,
dtype=tf.int32)
dummy_network = DummyNetwork()
tf.random.set_seed(0) # set seed for reproducibility
nb_batches = len(dataset) // 2
epochs = 10
for epoch in range(epochs):
print(f'Epoch {epoch + 1}/{epochs}')
for i, batch in enumerate(loader):
# get the data from batch and predict
x, sample_indices = batch[defs.KEY_IMAGES], batch[
defs.KEY_SAMPLE_INDEX]
prediction = dummy_network(x)
# translate the prediction to numpy
numpy_prediction = prediction.numpy()
# add the batch prediction to the assembler
is_last = i == nb_batches - 1
assembler.add_batch(numpy_prediction, sample_indices.numpy(),
is_last)
# process the subjects/images that are fully assembled
for subject_index in assembler.subjects_ready:
subject_prediction = assembler.get_assembled_subject(
subject_index)
# extract the target and image properties via direct extract
direct_sample = dataset.direct_extract(direct_extractor,
subject_index)
reference, image_properties = direct_sample[
defs.KEY_LABELS], direct_sample[defs.KEY_PROPERTIES]
# evaluate the prediction against the reference
evaluator.evaluate(subject_prediction[..., 0], reference[...,
0],
direct_sample[defs.KEY_SUBJECT])
# calculate mean and standard deviation of each metric
results = statistics_aggregator.calculate(evaluator.results)
# log to TensorBoard into category train
for result in results:
with tb.as_default():
tf.summary.scalar(f'train/{result.metric}-{result.id_}',
result.value, epoch)
# clear results such that the evaluator is ready for the next evaluation
evaluator.clear()
def __init__(self,
config: cfg.Configuration,
subjects_train,
subjects_valid,
subjects_test,
collate_fn=pymia_cnv.TorchCollate(
('images', 'labels', 'mask_fg', 'mask_t1h2o'))):
super().__init__()
indexing_strategy = pymia_extr.SliceIndexing()
self.dataset = pymia_extr.ParameterizableDataset(
config.database_file,
indexing_strategy,
pymia_extr.SubjectExtractor(), # for the usual select_indices
None)
self.no_subjects_train = len(subjects_train)
self.no_subjects_valid = len(subjects_valid)
self.no_subjects_test = 0
# get sampler ids by subjects
sampler_ids_train = pymia_extr.select_indices(
self.dataset, pymia_extr.SubjectSelection(subjects_train))
sampler_ids_valid = pymia_extr.select_indices(
self.dataset, pymia_extr.SubjectSelection(subjects_valid))
# define extractors
self.extractor_train = pymia_extr.ComposeExtractor([
pymia_extr.DataExtractor(categories=('images', 'labels')),
pymia_extr.IndexingExtractor(
), # for SubjectAssembler (assembling)
pymia_extr.ImageShapeExtractor() # for SubjectAssembler (shape)
])
self.extractor_valid = pymia_extr.ComposeExtractor([
pymia_extr.DataExtractor(categories=('images', 'labels')),
pymia_extr.IndexingExtractor(
), # for SubjectAssembler (assembling)
pymia_extr.ImageShapeExtractor() # for SubjectAssembler (shape)
])
self.extractor_test = pymia_extr.ComposeExtractor([
pymia_extr.SubjectExtractor(),
pymia_extr.DataExtractor(categories=('labels', )),
pymia_extr.ImagePropertiesExtractor(),
pymia_extr.ImageShapeExtractor()
])
# define transforms for extraction
self.extraction_transform_train = pymia_tfm.ComposeTransform([
pymia_tfm.SizeCorrection((cfg.TENSOR_WIDTH, cfg.TENSOR_HEIGHT)),
pymia_tfm.Permute((2, 0, 1)),
pymia_tfm.Squeeze(entries=('labels', ),
squeeze_axis=0), # for PyTorch loss functions
pymia_tfm.LambdaTransform(
lambda_fn=lambda np_data: np_data.astype(np.int64),
entries=('labels', )),
# for PyTorch loss functions
pymia_tfm.ToTorchTensor()
])
self.extraction_transform_valid = pymia_tfm.ComposeTransform([
pymia_tfm.SizeCorrection((cfg.TENSOR_WIDTH, cfg.TENSOR_HEIGHT)),
pymia_tfm.Permute((2, 0, 1)),
pymia_tfm.Squeeze(entries=('labels', ),
squeeze_axis=0), # for PyTorch loss functions
pymia_tfm.LambdaTransform(
lambda_fn=lambda np_data: np_data.astype(np.int64),
entries=('labels', )),
# for PyTorch loss functions
pymia_tfm.ToTorchTensor()
])
self.extraction_transform_test = None
# define loaders
training_sampler = pymia_extr.SubsetRandomSampler(sampler_ids_train)
self.loader_train = pymia_extr.DataLoader(self.dataset,
config.batch_size_training,
sampler=training_sampler,
collate_fn=collate_fn,
num_workers=1)
validation_sampler = pymia_extr.SubsetSequentialSampler(
sampler_ids_valid)
self.loader_valid = pymia_extr.DataLoader(self.dataset,
config.batch_size_testing,
sampler=validation_sampler,
collate_fn=collate_fn,
num_workers=1)
self.loader_test = None
def __init__(self,
config: cfg.Configuration,
subjects_train,
subjects_valid,
subjects_test,
is_subject_selection: bool = True,
collate_fn=lib_cnv.TensorFlowCollate(),
padding_size: tuple = (0, 0, 0)):
super().__init__()
indexing_strategy = pymia_extr.PatchWiseIndexing(
patch_shape=config.patch_size, ignore_incomplete=False)
self.dataset = pymia_extr.ParameterizableDataset(
config.database_file,
indexing_strategy,
pymia_extr.SubjectExtractor(), # for the select_indices
None)
self.no_subjects_train = len(subjects_train)
self.no_subjects_valid = len(subjects_valid)
self.no_subjects_test = len(subjects_test)
if is_subject_selection:
# get sampler ids by subjects
sampler_ids_train = pymia_extr.select_indices(
self.dataset, pymia_extr.SubjectSelection(subjects_train))
sampler_ids_valid = pymia_extr.select_indices(
self.dataset, pymia_extr.SubjectSelection(subjects_valid))
sampler_ids_test = pymia_extr.select_indices(
self.dataset, pymia_extr.SubjectSelection(subjects_test))
else:
# get sampler ids from indices files
sampler_ids_train, sampler_ids_valid, sampler_ids_test = pkl.load_sampler_ids(
config.indices_dir, pkl.PATCH_WISE_FILE_NAME, subjects_train,
subjects_valid, subjects_test)
# define extractors
self.extractor_train = pymia_extr.ComposeExtractor([
pymia_extr.NamesExtractor(), # required for SelectiveDataExtractor
pymia_extr.PadDataExtractor(
padding=padding_size,
extractor=pymia_extr.DataExtractor(
categories=(pymia_def.KEY_IMAGES, ))),
pymia_extr.PadDataExtractor(
padding=(0, 0, 0),
extractor=pymia_extr.SelectiveDataExtractor(
selection=config.maps, category=pymia_def.KEY_LABELS)),
pymia_extr.PadDataExtractor(padding=(0, 0, 0),
extractor=pymia_extr.DataExtractor(
categories=(defs.ID_MASK_FG,
defs.ID_MASK_T1H2O))),
pymia_extr.IndexingExtractor(),
pymia_extr.ImageShapeExtractor()
])
# to calculate validation loss, we require the labels and mask during validation
self.extractor_valid = pymia_extr.ComposeExtractor([
pymia_extr.NamesExtractor(), # required for SelectiveDataExtractor
pymia_extr.PadDataExtractor(
padding=padding_size,
extractor=pymia_extr.DataExtractor(
categories=(pymia_def.KEY_IMAGES, ))),
pymia_extr.PadDataExtractor(
padding=(0, 0, 0),
extractor=pymia_extr.SelectiveDataExtractor(
selection=config.maps, category=pymia_def.KEY_LABELS)),
pymia_extr.PadDataExtractor(
padding=(0, 0, 0),
extractor=pymia_extr.DataExtractor(
categories=(defs.ID_MASK_FG, defs.ID_MASK_T1H2O,
defs.ID_MASK_ROI, defs.ID_MASK_ROI_T1H2O))),
pymia_extr.IndexingExtractor(),
pymia_extr.ImageShapeExtractor()
])
self.extractor_test = pymia_extr.ComposeExtractor([
pymia_extr.NamesExtractor(), # required for SelectiveDataExtractor
pymia_extr.SubjectExtractor(),
pymia_extr.SelectiveDataExtractor(selection=config.maps,
category=pymia_def.KEY_LABELS),
pymia_extr.DataExtractor(categories=(defs.ID_MASK_FG,
defs.ID_MASK_T1H2O,
defs.ID_MASK_ROI,
defs.ID_MASK_ROI_T1H2O)),
pymia_extr.ImagePropertiesExtractor(),
pymia_extr.ImageShapeExtractor(),
ext.NormalizationExtractor()
])
# define transforms for extraction
# after extraction, the first dimension is the batch dimension.
# E.g., shape = (1, 16, 16, 4) instead of (16, 16, 4) --> therefore squeeze the data
self.extraction_transform_train = pymia_tfm.Squeeze(
entries=(pymia_def.KEY_IMAGES, pymia_def.KEY_LABELS,
defs.ID_MASK_FG, defs.ID_MASK_T1H2O),
squeeze_axis=0)
self.extraction_transform_valid = pymia_tfm.Squeeze(
entries=(pymia_def.KEY_IMAGES, pymia_def.KEY_LABELS,
defs.ID_MASK_FG, defs.ID_MASK_T1H2O),
squeeze_axis=0)
self.extraction_transform_test = None
# define loaders
training_sampler = pymia_extr.SubsetRandomSampler(sampler_ids_train)
self.loader_train = pymia_extr.DataLoader(self.dataset,
config.batch_size_training,
sampler=training_sampler,
collate_fn=collate_fn,
num_workers=1)
validation_sampler = pymia_extr.SubsetSequentialSampler(
sampler_ids_valid)
self.loader_valid = pymia_extr.DataLoader(self.dataset,
config.batch_size_testing,
sampler=validation_sampler,
collate_fn=collate_fn,
num_workers=1)
testing_sampler = pymia_extr.SubsetSequentialSampler(sampler_ids_test)
self.loader_test = pymia_extr.DataLoader(self.dataset,
config.batch_size_testing,
sampler=testing_sampler,
collate_fn=collate_fn,
num_workers=1)
def main(hdf_file: str):
extractor = extr.ComposeExtractor([
extr.NamesExtractor(),
extr.DataExtractor(),
extr.SelectiveDataExtractor(),
extr.DataExtractor(('numerical', ), ignore_indexing=True),
extr.DataExtractor(('gender', ), ignore_indexing=True),
extr.DataExtractor(('mask', ), ignore_indexing=False),
extr.SubjectExtractor(),
extr.FilesExtractor(categories=(defs.KEY_IMAGES, defs.KEY_LABELS,
'mask', 'numerical', 'gender')),
extr.IndexingExtractor(),
extr.ImagePropertiesExtractor()
])
dataset = extr.PymiaDatasource(hdf_file, extr.SliceIndexing(), extractor)
for i in range(len(dataset)):
item = dataset[i]
index_expr = item[defs.KEY_INDEX_EXPR] # type: data.IndexExpression
root = item[defs.KEY_FILE_ROOT]
image = None # type: sitk.Image
for i, file in enumerate(
item[defs.KEY_PLACEHOLDER_FILES.format('images')]):
image = sitk.ReadImage(os.path.join(root, file))
np_img = sitk.GetArrayFromImage(image).astype(np.float32)
np_img = (np_img - np_img.mean()) / np_img.std()
np_slice = np_img[index_expr.expression]
if (np_slice != item[defs.KEY_IMAGES][..., i]).any():
raise ValueError('slice not equal')
# for any image
image_properties = conv.ImageProperties(image)
if image_properties != item[defs.KEY_PROPERTIES]:
raise ValueError('image properties not equal')
for file in item[defs.KEY_PLACEHOLDER_FILES.format('labels')]:
image = sitk.ReadImage(os.path.join(root, file))
np_img = sitk.GetArrayFromImage(image)
np_img = np.expand_dims(
np_img, axis=-1
) # due to the convention of having the last dim as number of channels
np_slice = np_img[index_expr.expression]
if (np_slice != item[defs.KEY_LABELS]).any():
raise ValueError('slice not equal')
for file in item[defs.KEY_PLACEHOLDER_FILES.format('mask')]:
image = sitk.ReadImage(os.path.join(root, file))
np_img = sitk.GetArrayFromImage(image)
np_img = np.expand_dims(
np_img, axis=-1
) # due to the convention of having the last dim as number of channels
np_slice = np_img[index_expr.expression]
if (np_slice != item['mask']).any():
raise ValueError('slice not equal')
for file in item[defs.KEY_PLACEHOLDER_FILES.format('numerical')]:
with open(os.path.join(root, file), 'r') as f:
lines = f.readlines()
age = float(lines[0].split(':')[1].strip())
gpa = float(lines[1].split(':')[1].strip())
if age != item['numerical'][0][0] or gpa != item['numerical'][0][1]:
raise ValueError('value not equal')
for file in item[defs.KEY_PLACEHOLDER_FILES.format('gender')]:
with open(os.path.join(root, file), 'r') as f:
gender = f.readlines()[2].split(':')[1].strip()
if gender != str(item['gender'][0]):
raise ValueError('value not equal')
print('All test passed!')
def main(config_file: str):
config = cfg.load(config_file, cfg.Configuration)
print(config)
indexing_strategy = pymia_extr.SliceIndexing() # slice-wise extraction
extraction_transform = None # we do not want to apply any transformation on the slices after extraction
# define an extractor for training, i.e. what information we would like to extract per sample
train_extractor = pymia_extr.ComposeExtractor([pymia_extr.NamesExtractor(),
pymia_extr.DataExtractor(),
pymia_extr.SelectiveDataExtractor()])
# define an extractor for testing, i.e. what information we would like to extract per sample
# not that usually we don't use labels for testing, i.e. the SelectiveDataExtractor is only used for this example
test_extractor = pymia_extr.ComposeExtractor([pymia_extr.NamesExtractor(),
pymia_extr.IndexingExtractor(),
pymia_extr.DataExtractor(),
pymia_extr.SelectiveDataExtractor(),
pymia_extr.ImageShapeExtractor()])
# define an extractor for evaluation, i.e. what information we would like to extract per sample
eval_extractor = pymia_extr.ComposeExtractor([pymia_extr.NamesExtractor(),
pymia_extr.SubjectExtractor(),
pymia_extr.SelectiveDataExtractor(),
pymia_extr.ImagePropertiesExtractor()])
# define the data set
dataset = pymia_extr.ParameterizableDataset(config.database_file,
indexing_strategy,
pymia_extr.SubjectExtractor(), # for select_indices() below
extraction_transform)
# generate train / test split for data set
# we use Subject_0, Subject_1 and Subject_2 for training and Subject_3 for testing
sampler_ids_train = pymia_extr.select_indices(dataset,
pymia_extr.SubjectSelection(('Subject_0', 'Subject_1', 'Subject_2')))
sampler_ids_test = pymia_extr.select_indices(dataset,
pymia_extr.SubjectSelection(('Subject_3')))
# set up training data loader
training_sampler = pymia_extr.SubsetRandomSampler(sampler_ids_train)
training_loader = pymia_extr.DataLoader(dataset, config.batch_size_training, sampler=training_sampler,
collate_fn=collate_batch, num_workers=1)
# set up testing data loader
testing_sampler = pymia_extr.SubsetSequentialSampler(sampler_ids_test)
testing_loader = pymia_extr.DataLoader(dataset, config.batch_size_testing, sampler=testing_sampler,
collate_fn=collate_batch, num_workers=1)
sample = dataset.direct_extract(train_extractor, 0) # extract a subject
evaluator = init_evaluator() # initialize evaluator
for epoch in range(config.epochs): # epochs loop
dataset.set_extractor(train_extractor)
for batch in training_loader: # batches for training
# feed_dict = batch_to_feed_dict(x, y, batch, True) # e.g. for TensorFlow
# train model, e.g.:
# sess.run([train_op, loss], feed_dict=feed_dict)
pass
# subject assembler for testing
subject_assembler = pymia_asmbl.SubjectAssembler()
dataset.set_extractor(test_extractor)
for batch in testing_loader: # batches for testing
# feed_dict = batch_to_feed_dict(x, y, batch, False) # e.g. for TensorFlow
# test model, e.g.:
# prediction = sess.run(y_model, feed_dict=feed_dict)
prediction = np.stack(batch['labels'], axis=0) # we use the labels as predictions such that we can validate the assembler
subject_assembler.add_batch(prediction, batch)
# evaluate all test images
for subject_idx in list(subject_assembler.predictions.keys()):
# convert prediction and labels back to SimpleITK images
sample = dataset.direct_extract(eval_extractor, subject_idx)
label_image = pymia_conv.NumpySimpleITKImageBridge.convert(sample['labels'],
sample['properties'])
assembled = subject_assembler.get_assembled_subject(sample['subject_index'])
prediction_image = pymia_conv.NumpySimpleITKImageBridge.convert(assembled, sample['properties'])
evaluator.evaluate(prediction_image, label_image, sample['subject']) # evaluate prediction
def main(hdf_file: str):
extractor = extr.ComposeExtractor([
extr.NamesExtractor(),
extr.DataExtractor(),
extr.SelectiveDataExtractor(),
extr.DataExtractor(('numerical', ), ignore_indexing=True),
extr.DataExtractor(('sex', ), ignore_indexing=True),
extr.DataExtractor(('mask', ), ignore_indexing=False),
extr.SubjectExtractor(),
extr.FilesExtractor(categories=('images', 'labels', 'mask',
'numerical', 'sex')),
extr.IndexingExtractor(),
extr.ImagePropertiesExtractor()
])
dataset = extr.ParameterizableDataset(hdf_file, extr.SliceIndexing(),
extractor)
for i in range(len(dataset)):
item = dataset[i]
index_expr = item['index_expr'] # type: pymia_data.IndexExpression
root = item['file_root']
image = None # type: sitk.Image
for i, file in enumerate(item['images_files']):
image = sitk.ReadImage(os.path.join(root, file))
np_img = sitk.GetArrayFromImage(image).astype(np.float32)
np_img = (np_img - np_img.mean()) / np_img.std()
np_slice = np_img[index_expr.expression]
if (np_slice != item['images'][..., i]).any():
raise ValueError('slice not equal')
# for any image
image_properties = conv.ImageProperties(image)
if image_properties != item['properties']:
raise ValueError('image properties not equal')
for file in item['labels_files']:
image = sitk.ReadImage(os.path.join(root, file))
np_img = sitk.GetArrayFromImage(image)
np_img = np.expand_dims(
np_img, axis=-1
) # due to the convention of having the last dim as number of channels
np_slice = np_img[index_expr.expression]
if (np_slice != item['labels']).any():
raise ValueError('slice not equal')
for file in item['mask_files']:
image = sitk.ReadImage(os.path.join(root, file))
np_img = sitk.GetArrayFromImage(image)
np_img = np.expand_dims(
np_img, axis=-1
) # due to the convention of having the last dim as number of channels
np_slice = np_img[index_expr.expression]
if (np_slice != item['mask']).any():
raise ValueError('slice not equal')
for file in item['numerical_files']:
with open(os.path.join(root, file), 'r') as f:
lines = f.readlines()
age = float(lines[0].split(':')[1].strip())
gpa = float(lines[1].split(':')[1].strip())
if age != item['numerical'][0][0] or gpa != item['numerical'][0][1]:
raise ValueError('value not equal')
for file in item['sex_files']:
with open(os.path.join(root, file), 'r') as f:
sex = f.readlines()[2].split(':')[1].strip()
if sex != str(item['sex'][0]):
raise ValueError('value not equal')
print('All test passed!')
def main(hdf_file: str, log_dir: str):
# initialize the evaluator with the metrics and the labels to evaluate
metrics = [metric.DiceCoefficient()]
labels = {1: 'WHITEMATTER', 2: 'GREYMATTER', 5: 'THALAMUS'}
evaluator = eval_.SegmentationEvaluator(metrics, labels)
# we want to log the mean and standard deviation of the metrics among all subjects of the dataset
functions = {'MEAN': np.mean, 'STD': np.std}
statistics_aggregator = writer.StatisticsAggregator(functions=functions)
# initialize TensorBoard writer
tb = tensorboard.SummaryWriter(
os.path.join(log_dir, 'logging-example-torch'))
# initialize the data handling
transform = tfm.Permute(permutation=(2, 0, 1), entries=(defs.KEY_IMAGES, ))
dataset = extr.PymiaDatasource(
hdf_file, extr.SliceIndexing(),
extr.DataExtractor(categories=(defs.KEY_IMAGES, )), transform)
pytorch_dataset = pymia_torch.PytorchDatasetAdapter(dataset)
loader = torch_data.dataloader.DataLoader(pytorch_dataset,
batch_size=100,
shuffle=False)
assembler = assm.SubjectAssembler(dataset)
direct_extractor = extr.ComposeExtractor([
extr.SubjectExtractor(), # extraction of the subject name
extr.ImagePropertiesExtractor(
), # Extraction of image properties (origin, spacing, etc.) for storage
extr.DataExtractor(
categories=(defs.KEY_LABELS,
)) # Extraction of "labels" entries for evaluation
])
# initialize a dummy network, which returns a random prediction
class DummyNetwork(nn.Module):
def forward(self, x):
return torch.randint(0, 6, (x.size(0), 1, *x.size()[2:]))
dummy_network = DummyNetwork()
torch.manual_seed(0) # set seed for reproducibility
nb_batches = len(loader)
epochs = 10
for epoch in range(epochs):
print(f'Epoch {epoch + 1}/{epochs}')
for i, batch in enumerate(loader):
# get the data from batch and predict
x, sample_indices = batch[defs.KEY_IMAGES], batch[
defs.KEY_SAMPLE_INDEX]
prediction = dummy_network(x)
# translate the prediction to numpy and back to (B)HWC (channel last)
numpy_prediction = prediction.numpy().transpose((0, 2, 3, 1))
# add the batch prediction to the assembler
is_last = i == nb_batches - 1
assembler.add_batch(numpy_prediction, sample_indices.numpy(),
is_last)
# process the subjects/images that are fully assembled
for subject_index in assembler.subjects_ready:
subject_prediction = assembler.get_assembled_subject(
subject_index)
# extract the target and image properties via direct extract
direct_sample = dataset.direct_extract(direct_extractor,
subject_index)
reference, image_properties = direct_sample[
defs.KEY_LABELS], direct_sample[defs.KEY_PROPERTIES]
# evaluate the prediction against the reference
evaluator.evaluate(subject_prediction[..., 0], reference[...,
0],
direct_sample[defs.KEY_SUBJECT])
# calculate mean and standard deviation of each metric
results = statistics_aggregator.calculate(evaluator.results)
# log to TensorBoard into category train
for result in results:
tb.add_scalar(f'train/{result.metric}-{result.id_}', result.value,
epoch)
# clear results such that the evaluator is ready for the next evaluation
evaluator.clear()
def main(hdf_file, log_dir):
# initialize the evaluator with the metrics and the labels to evaluate
metrics = [metric.DiceCoefficient()]
labels = {
1: 'WHITEMATTER',
2: 'GREYMATTER',
3: 'HIPPOCAMPUS',
4: 'AMYGDALA',
5: 'THALAMUS'
}
evaluator = eval_.SegmentationEvaluator(metrics, labels)
# we want to log the mean and standard deviation of the metrics among all subjects of the dataset
functions = {'MEAN': np.mean, 'STD': np.std}
statistics_aggregator = writer.StatisticsAggregator(functions=functions)
console_writer = writer.ConsoleStatisticsWriter(functions=functions)
# initialize TensorBoard writer
summary_writer = tf.summary.create_file_writer(
os.path.join(log_dir, 'logging-example-tensorflow'))
# setup the training datasource
train_subjects, valid_subjects = ['Subject_1', 'Subject_2',
'Subject_3'], ['Subject_4']
extractor = extr.DataExtractor(categories=(defs.KEY_IMAGES,
defs.KEY_LABELS))
indexing_strategy = extr.SliceIndexing()
augmentation_transforms = [
augm.RandomElasticDeformation(),
augm.RandomMirror()
]
transforms = [tfm.Squeeze(entries=(defs.KEY_LABELS, ))]
train_transforms = tfm.ComposeTransform(augmentation_transforms +
transforms)
train_dataset = extr.PymiaDatasource(hdf_file,
indexing_strategy,
extractor,
train_transforms,
subject_subset=train_subjects)
# setup the validation datasource
batch_size = 16
valid_transforms = tfm.ComposeTransform([])
valid_dataset = extr.PymiaDatasource(hdf_file,
indexing_strategy,
extractor,
valid_transforms,
subject_subset=valid_subjects)
direct_extractor = extr.ComposeExtractor([
extr.SubjectExtractor(),
extr.ImagePropertiesExtractor(),
extr.DataExtractor(categories=(defs.KEY_LABELS, ))
])
assembler = assm.SubjectAssembler(valid_dataset)
# tensorflow specific handling
train_gen_fn = pymia_tf.get_tf_generator(train_dataset)
tf_train_dataset = tf.data.Dataset.from_generator(
generator=train_gen_fn,
output_types={
defs.KEY_IMAGES: tf.float32,
defs.KEY_LABELS: tf.int64,
defs.KEY_SAMPLE_INDEX: tf.int64
})
tf_train_dataset = tf_train_dataset.batch(batch_size).shuffle(
len(train_dataset))
valid_gen_fn = pymia_tf.get_tf_generator(valid_dataset)
tf_valid_dataset = tf.data.Dataset.from_generator(
generator=valid_gen_fn,
output_types={
defs.KEY_IMAGES: tf.float32,
defs.KEY_LABELS: tf.int64,
defs.KEY_SAMPLE_INDEX: tf.int64
})
tf_valid_dataset = tf_valid_dataset.batch(batch_size)
u_net = unet.build_model(channels=2,
num_classes=6,
layer_depth=3,
filters_root=16)
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)
train_loss = tf.keras.metrics.Mean('train_loss', dtype=tf.float32)
train_batches = len(train_dataset) // batch_size
# looping over the data in the dataset
epochs = 100
for epoch in range(epochs):
print(f'Epoch {epoch + 1}/{epochs}')
# training
print('training')
for i, batch in enumerate(tf_train_dataset):
x, y = batch[defs.KEY_IMAGES], batch[defs.KEY_LABELS]
with tf.GradientTape() as tape:
logits = u_net(x, training=True)
loss = tf.keras.losses.sparse_categorical_crossentropy(
y, logits, from_logits=True)
grads = tape.gradient(loss, u_net.trainable_variables)
optimizer.apply_gradients(zip(grads, u_net.trainable_variables))
train_loss(loss)
with summary_writer.as_default():
tf.summary.scalar('train/loss',
train_loss.result(),
step=epoch * train_batches + i)
print(
f'[{i + 1}/{train_batches}]\tloss: {train_loss.result().numpy()}'
)
# validation
print('validation')
valid_batches = len(valid_dataset) // batch_size
for i, batch in enumerate(tf_valid_dataset):
x, sample_indices = batch[defs.KEY_IMAGES], batch[
defs.KEY_SAMPLE_INDEX]
logits = u_net(x)
prediction = tf.expand_dims(tf.math.argmax(logits, -1), -1)
numpy_prediction = prediction.numpy()
is_last = i == valid_batches - 1
assembler.add_batch(numpy_prediction, sample_indices.numpy(),
is_last)
for subject_index in assembler.subjects_ready:
subject_prediction = assembler.get_assembled_subject(
subject_index)
direct_sample = train_dataset.direct_extract(
direct_extractor, subject_index)
target, image_properties = direct_sample[
defs.KEY_LABELS], direct_sample[defs.KEY_PROPERTIES]
# evaluate the prediction against the reference
evaluator.evaluate(subject_prediction[..., 0], target[..., 0],
direct_sample[defs.KEY_SUBJECT])
# calculate mean and standard deviation of each metric
results = statistics_aggregator.calculate(evaluator.results)
# log to TensorBoard into category train
with summary_writer.as_default():
for result in results:
tf.summary.scalar(f'valid/{result.metric}-{result.id_}',
result.value, epoch)
console_writer.write(evaluator.results)
# clear results such that the evaluator is ready for the next evaluation
evaluator.clear()
