def main(hdf_file: str, save_dir: str):
mask = data.ID_MASK_FG
patch_shape = (1, 32, 32) # this corresponds to the output patch size
indexing_strategy = pymia_idx.PatchWiseIndexing(patch_shape=patch_shape,
ignore_incomplete=False)
file_str = pkl.PATCH_WISE_FILE_NAME
dataset = pymia_extr.ParameterizableDataset(
hdf_file, indexing_strategy,
pymia_extr.ComposeExtractor([
pymia_extr.SubjectExtractor(),
pymia_extr.DataExtractor(categories=(mask, ))
]))
os.makedirs(save_dir, exist_ok=True)
for subject in dataset.get_subjects():
print(subject)
selection = pymia_extr.ComposeSelection([
pymia_extr.SubjectSelection(subject),
WithForegroundSelectionByMask(mask)
])
ids = pymia_extr.select_indices(dataset, selection)
pkl.dump_indices_file(save_dir, file_str.format(subject), ids)
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 __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(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