def initialise_resize_aggregator(self):
self.output_decoder = ResizeSamplesAggregator(
image_reader=self.readers[0],
output_path=self.action_param.save_seg_dir,
window_border=self.action_param.border,
interp_order=self.action_param.output_interp_order,
postfix=self.action_param.output_postfix)
def test_25d_init(self):
reader = get_25d_reader()
sampler = ResizeSampler(reader=reader,
window_sizes=SINGLE_25D_DATA,
batch_size=1,
shuffle=False,
queue_length=50)
aggregator = ResizeSamplesAggregator(
image_reader=reader,
name='image',
output_path=os.path.join('testing_data', 'aggregated'),
interp_order=3)
more_batch = True
with self.test_session() as sess:
sampler.set_num_threads(2)
while more_batch:
try:
out = sess.run(sampler.pop_batch_op())
except tf.errors.OutOfRangeError:
break
more_batch = aggregator.decode_batch(
out['image'], out['image_location'])
output_filename = '{}_niftynet_out.nii.gz'.format(
sampler.reader.get_subject_id(0))
output_file = os.path.join('testing_data',
'aggregated',
output_filename)
self.assertAllClose(
nib.load(output_file).shape, [255, 168, 256, 1, 1],
rtol=1e-03, atol=1e-03)
sampler.close_all()
def test_inverse_mapping(self):
reader = get_label_reader()
sampler = ResizeSampler(reader=reader,
data_param=MOD_LABEL_DATA,
batch_size=1,
shuffle_buffer=False,
queue_length=50)
aggregator = ResizeSamplesAggregator(image_reader=reader,
name='label',
output_path=os.path.join(
'testing_data', 'aggregated'),
interp_order=0)
more_batch = True
with self.test_session() as sess:
coordinator = tf.train.Coordinator()
sampler.run_threads(sess, coordinator, num_threads=2)
while more_batch:
out = sess.run(sampler.pop_batch_op())
more_batch = aggregator.decode_batch(out['label'],
out['label_location'])
output_filename = '{}_niftynet_out.nii.gz'.format(
sampler.reader.get_subject_id(0))
output_file = os.path.join('testing_data', 'aggregated',
output_filename)
self.assertAllClose(nib.load(output_file).shape, [256, 168, 256, 1, 1])
sampler.close_all()
def test_inverse_mapping(self):
reader = get_label_reader()
sampler = ResizeSampler(reader=reader,
window_sizes=MOD_LABEL_DATA,
batch_size=1,
shuffle=False,
queue_length=50)
aggregator = ResizeSamplesAggregator(image_reader=reader,
name='label',
output_path=os.path.join(
'testing_data', 'aggregated'),
interp_order=0)
more_batch = True
with self.cached_session() as sess:
sampler.set_num_threads(2)
while more_batch:
try:
out = sess.run(sampler.pop_batch_op())
except tf.errors.OutOfRangeError:
break
more_batch = aggregator.decode_batch(
{'window_label': out['label']}, out['label_location'])
output_filename = 'window_label_{}_niftynet_out.nii.gz'.format(
sampler.reader.get_subject_id(0))
output_file = os.path.join('testing_data', 'aggregated',
output_filename)
self.assertAllClose(nib.load(output_file).shape, [256, 168, 256])
sampler.close_all()
def test_init_2d_mo_bidimcsv(self):
reader = get_2d_reader()
sampler = ResizeSampler(reader=reader,
window_sizes=MOD_2D_DATA,
batch_size=1,
shuffle=False,
queue_length=50)
aggregator = ResizeSamplesAggregator(image_reader=reader,
name='image',
output_path=os.path.join(
'testing_data', 'aggregated'),
interp_order=3)
more_batch = True
with self.cached_session() as sess:
sampler.set_num_threads(2)
while more_batch:
try:
out = sess.run(sampler.pop_batch_op())
except tf.errors.OutOfRangeError:
break
min_val = np.sum((np.asarray(out['image']).flatten()))
stats_val = [
np.min(out['image']),
np.max(out['image']),
np.sum(out['image'])
]
stats_val = np.expand_dims(stats_val, 0)
stats_val = np.concatenate([stats_val, stats_val], axis=0)
more_batch = aggregator.decode_batch(
{
'window_image': out['image'],
'csv_sum': min_val,
'csv_stats_2d': stats_val
}, out['image_location'])
output_filename = 'window_image_{}_niftynet_out.nii.gz'.format(
sampler.reader.get_subject_id(0))
sum_filename = os.path.join(
'testing_data', 'aggregated', 'csv_sum_{}_niftynet_out.csv'.format(
sampler.reader.get_subject_id(0)))
stats_filename = os.path.join(
'testing_data', 'aggregated',
'csv_stats_2d_{}_niftynet_out.csv'.format(
sampler.reader.get_subject_id(0)))
output_file = os.path.join('testing_data', 'aggregated',
output_filename)
self.assertAllClose(nib.load(output_file).shape, (128, 128))
min_pd = pd.read_csv(sum_filename)
self.assertAllClose(min_pd.shape, [1, 2])
stats_pd = pd.read_csv(stats_filename)
self.assertAllClose(stats_pd.shape, [1, 7])
sampler.close_all()
def initialise_resize_aggregator(self):
'''
Define the resize aggregator used for decoding using the
configuration parameters
:return:
'''
self.output_decoder = ResizeSamplesAggregator(
image_reader=self.readers[0],
output_path=self.action_param.save_seg_dir,
window_border=self.action_param.border,
interp_order=self.action_param.output_interp_order,
postfix=self.action_param.output_postfix)
def test_3d_init_mo_3out(self):
reader = get_3d_reader()
sampler = ResizeSampler(reader=reader,
window_sizes=MULTI_MOD_DATA,
batch_size=1,
shuffle=False,
queue_length=50)
aggregator = ResizeSamplesAggregator(image_reader=reader,
name='image',
output_path=os.path.join(
'testing_data', 'aggregated'),
interp_order=3)
more_batch = True
with self.cached_session() as sess:
sampler.set_num_threads(2)
while more_batch:
try:
out = sess.run(sampler.pop_batch_op())
except tf.errors.OutOfRangeError:
break
sum_val = np.sum(out['image'])
stats_val = [
np.sum(out['image']),
np.min(out['image']),
np.max(out['image'])
]
more_batch = aggregator.decode_batch(
{
'window_image': out['image'],
'csv_sum': sum_val,
'csv_stats': stats_val
}, out['image_location'])
output_filename = 'window_image_{}_niftynet_out.nii.gz'.format(
sampler.reader.get_subject_id(0))
sum_filename = os.path.join(
'testing_data', 'aggregated', 'csv_sum_{}_niftynet_out.csv'.format(
sampler.reader.get_subject_id(0)))
stats_filename = os.path.join(
'testing_data', 'aggregated',
'csv_stats_{}_niftynet_out.csv'.format(
sampler.reader.get_subject_id(0)))
output_file = os.path.join('testing_data', 'aggregated',
output_filename)
self.assertAllClose(nib.load(output_file).shape, (256, 168, 256, 1, 2))
sum_pd = pd.read_csv(sum_filename)
self.assertAllClose(sum_pd.shape, [1, 2])
stats_pd = pd.read_csv(stats_filename)
self.assertAllClose(stats_pd.shape, [1, 4])
sampler.close_all()
def connect_data_and_network(self,
outputs_collector=None,
gradients_collector=None):
def switch_samplers(for_training):
with tf.name_scope('train' if for_training else 'validation'):
sampler = self.get_sampler()[0 if for_training else -1]
return sampler() # returns image only
if self.is_training:
self.patience = self.action_param.patience
self.mode = self.action_param.early_stopping_mode
if self.action_param.validation_every_n > 0:
sampler_window = \
tf.cond(tf.logical_not(self.is_validation),
lambda: switch_samplers(True),
lambda: switch_samplers(False))
else:
sampler_window = switch_samplers(True)
image_windows, _ = sampler_window
# image_windows, locations = sampler_window
# decode channels for moving and fixed images
image_windows_list = [
tf.expand_dims(img, axis=-1)
for img in tf.unstack(image_windows, axis=-1)
]
fixed_image, fixed_label, moving_image, moving_label = \
image_windows_list
# estimate ddf
dense_field = self.net(fixed_image, moving_image)
if isinstance(dense_field, tuple):
dense_field = dense_field[0]
# transform the moving labels
resampler = ResamplerLayer(interpolation='linear',
boundary='replicate')
resampled_moving_label = resampler(moving_label, dense_field)
# compute label loss (foreground only)
loss_func = LossFunction(n_class=1,
loss_type=self.action_param.loss_type,
softmax=False)
label_loss = loss_func(prediction=resampled_moving_label,
ground_truth=fixed_label)
dice_fg = 1.0 - label_loss
# appending regularisation loss
total_loss = label_loss
reg_loss = tf.get_collection('bending_energy')
if reg_loss:
total_loss = total_loss + \
self.net_param.decay * tf.reduce_mean(reg_loss)
self.total_loss = total_loss
# compute training gradients
with tf.name_scope('Optimiser'):
optimiser_class = OptimiserFactory.create(
name=self.action_param.optimiser)
self.optimiser = optimiser_class.get_instance(
learning_rate=self.action_param.lr)
grads = self.optimiser.compute_gradients(
total_loss, colocate_gradients_with_ops=True)
gradients_collector.add_to_collection(grads)
metrics_dice = loss_func(
prediction=tf.to_float(resampled_moving_label >= 0.5),
ground_truth=tf.to_float(fixed_label >= 0.5))
metrics_dice = 1.0 - metrics_dice
# command line output
outputs_collector.add_to_collection(var=dice_fg,
name='one_minus_data_loss',
collection=CONSOLE)
outputs_collector.add_to_collection(var=tf.reduce_mean(reg_loss),
name='bending_energy',
collection=CONSOLE)
outputs_collector.add_to_collection(var=total_loss,
name='total_loss',
collection=CONSOLE)
outputs_collector.add_to_collection(var=metrics_dice,
name='ave_fg_dice',
collection=CONSOLE)
# for tensorboard
outputs_collector.add_to_collection(var=dice_fg,
name='data_loss',
average_over_devices=True,
summary_type='scalar',
collection=TF_SUMMARIES)
outputs_collector.add_to_collection(var=total_loss,
name='total_loss',
average_over_devices=True,
summary_type='scalar',
collection=TF_SUMMARIES)
outputs_collector.add_to_collection(
var=metrics_dice,
name='averaged_foreground_Dice',
average_over_devices=True,
summary_type='scalar',
collection=TF_SUMMARIES)
# for visualisation debugging
# resampled_moving_image = resampler(moving_image, dense_field)
# outputs_collector.add_to_collection(
# var=fixed_image, name='fixed_image',
# collection=NETWORK_OUTPUT)
# outputs_collector.add_to_collection(
# var=fixed_label, name='fixed_label',
# collection=NETWORK_OUTPUT)
# outputs_collector.add_to_collection(
# var=moving_image, name='moving_image',
# collection=NETWORK_OUTPUT)
# outputs_collector.add_to_collection(
# var=moving_label, name='moving_label',
# collection=NETWORK_OUTPUT)
# outputs_collector.add_to_collection(
# var=resampled_moving_image, name='resampled_image',
# collection=NETWORK_OUTPUT)
# outputs_collector.add_to_collection(
# var=resampled_moving_label, name='resampled_label',
# collection=NETWORK_OUTPUT)
# outputs_collector.add_to_collection(
# var=dense_field, name='ddf', collection=NETWORK_OUTPUT)
# outputs_collector.add_to_collection(
# var=locations, name='locations', collection=NETWORK_OUTPUT)
# outputs_collector.add_to_collection(
# var=shift[0], name='a', collection=CONSOLE)
# outputs_collector.add_to_collection(
# var=shift[1], name='b', collection=CONSOLE)
else:
image_windows, locations = self.sampler()
image_windows_list = [
tf.expand_dims(img, axis=-1)
for img in tf.unstack(image_windows, axis=-1)
]
fixed_image, fixed_label, moving_image, moving_label = \
image_windows_list
dense_field = self.net(fixed_image, moving_image)
if isinstance(dense_field, tuple):
dense_field = dense_field[0]
# transform the moving labels
resampler = ResamplerLayer(interpolation='linear',
boundary='replicate')
resampled_moving_image = resampler(moving_image, dense_field)
resampled_moving_label = resampler(moving_label, dense_field)
outputs_collector.add_to_collection(var=fixed_image,
name='fixed_image',
collection=NETWORK_OUTPUT)
outputs_collector.add_to_collection(var=moving_image,
name='moving_image',
collection=NETWORK_OUTPUT)
outputs_collector.add_to_collection(var=resampled_moving_image,
name='resampled_moving_image',
collection=NETWORK_OUTPUT)
outputs_collector.add_to_collection(var=resampled_moving_label,
name='resampled_moving_label',
collection=NETWORK_OUTPUT)
outputs_collector.add_to_collection(var=fixed_label,
name='fixed_label',
collection=NETWORK_OUTPUT)
outputs_collector.add_to_collection(var=moving_label,
name='moving_label',
collection=NETWORK_OUTPUT)
#outputs_collector.add_to_collection(
# var=dense_field, name='field',
# collection=NETWORK_OUTPUT)
outputs_collector.add_to_collection(var=locations,
name='locations',
collection=NETWORK_OUTPUT)
self.output_decoder = ResizeSamplesAggregator(
image_reader=self.readers[0], # fixed image reader
name='fixed_image',
output_path=self.action_param.save_seg_dir,
interp_order=self.action_param.output_interp_order)
