def test_2d_init(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.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, [128, 128, 1, 1, 1])
sampler.close_all()
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 = WindowAsImageAggregator(
image_reader=reader,
output_path=os.path.join('testing_data', 'aggregated_identity'),
)
more_batch = True
out_shape = []
with self.cached_session() as sess:
sampler.set_num_threads(2)
while more_batch:
try:
out = sess.run(sampler.pop_batch_op())
out_shape = out['image'].shape[1:] + (1, )
except tf.errors.OutOfRangeError:
break
more_batch = aggregator.decode_batch(
{'window_image': out['image']}, out['image_location'])
output_filename = '{}_window_image_niftynet_generated.nii.gz'.format(
sampler.reader.get_subject_id(0))
output_file = os.path.join('testing_data', 'aggregated_identity',
output_filename)
out_shape = [out_shape[i] for i in NEW_ORDER_2D] + [
1,
]
self.assertAllClose(nib.load(output_file).shape, out_shape[:2])
sampler.close_all()
def initialise_resize_sampler(self):
self.sampler = [[ResizeSampler(
reader=reader,
window_sizes=self.data_param,
batch_size=self.net_param.batch_size,
shuffle=self.is_training,
smaller_final_batch_mode=self.net_param.smaller_final_batch_mode,
queue_length=self.net_param.queue_length) for reader in
self.readers]]
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 = WindowAsImageAggregator(
image_reader=reader,
output_path=os.path.join('testing_data', 'aggregated_identity'),
)
more_batch = True
out_shape = []
with self.cached_session() as sess:
sampler.set_num_threads(2)
while more_batch:
try:
out = sess.run(sampler.pop_batch_op())
out_shape = out['image'].shape[1:] + (1, )
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_stats2d': stats_val
}, out['image_location'])
output_filename = '{}_window_image_niftynet_generated.nii.gz'.format(
sampler.reader.get_subject_id(0))
sum_filename = os.path.join(
'testing_data', 'aggregated_identity',
'{}_csv_sum_niftynet_generated.csv'.format(
sampler.reader.get_subject_id(0)))
stats_filename = os.path.join(
'testing_data', 'aggregated_identity',
'{}_csv_stats2d_niftynet_generated.csv'.format(
sampler.reader.get_subject_id(0)))
output_file = os.path.join('testing_data', 'aggregated_identity',
output_filename)
out_shape = [out_shape[i] for i in NEW_ORDER_2D] + [
1,
]
self.assertAllClose(nib.load(output_file).shape, out_shape[:2])
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 test_2d_init(self):
sampler = ResizeSampler(reader=get_2d_reader(),
window_sizes=MOD_2D_DATA,
batch_size=1,
shuffle=True,
queue_length=1)
with self.cached_session() as sess:
sampler.set_num_threads(2)
out = sess.run(sampler.pop_batch_op())
self.assertAllClose(out['image'].shape, [1, 10, 9, 1])
sampler.close_all()
def test_dynamic_init(self):
sampler = ResizeSampler(reader=get_dynamic_window_reader(),
window_sizes=DYNAMIC_MOD_DATA,
batch_size=1,
shuffle=False,
queue_length=1)
with self.cached_session() as sess:
sampler.set_num_threads(2)
out = sess.run(sampler.pop_batch_op())
self.assertAllClose(out['image'].shape, [1, 8, 2, 256, 2])
sampler.close_all()
def test_3d_init(self):
sampler = ResizeSampler(reader=get_3d_reader(),
window_sizes=MULTI_MOD_DATA,
batch_size=1,
shuffle=False,
queue_length=1)
with self.test_session() as sess:
sampler.set_num_threads(2)
out = sess.run(sampler.pop_batch_op())
self.assertAllClose(out['image'].shape, [1, 7, 10, 2, 2])
sampler.close_all()
def test_init_3d_mo_bidimcsv(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
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, (256, 168, 256, 1, 2))
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_sampler(self):
self.sampler = []
if self.is_training:
self.sampler.append([
ResizeSampler(reader=reader,
csv_reader=None,
window_sizes=self.data_param,
batch_size=self.net_param.batch_size,
windows_per_image=1,
shuffle=True,
queue_length=self.net_param.queue_length)
for reader in self.readers
])
return
if self._infer_type in ('encode', 'encode-decode'):
self.sampler.append([
ResizeSampler(reader=reader,
csv_reader=None,
window_sizes=self.data_param,
batch_size=self.net_param.batch_size,
windows_per_image=1,
shuffle=False,
queue_length=self.net_param.queue_length)
for reader in self.readers
])
return
if self._infer_type == 'linear_interpolation':
self.sampler.append([
LinearInterpolateSampler(
reader=reader,
csv_reader=None,
window_sizes=self.data_param,
batch_size=self.net_param.batch_size,
n_interpolations=self.autoencoder_param.n_interpolations,
queue_length=self.net_param.queue_length)
for reader in self.readers
])
return
def initialise_sampler(self):
self.sampler = []
if self.is_training:
self.sampler.append([
ResizeSampler(reader=reader,
window_sizes=self.data_param,
batch_size=self.net_param.batch_size,
windows_per_image=1,
shuffle=True,
queue_length=self.net_param.queue_length)
for reader in self.readers
])
else:
self.sampler.append([
RandomVectorSampler(
names=('vector', ),
vector_size=(self.gan_param.noise_size, ),
batch_size=self.net_param.batch_size,
n_interpolations=self.gan_param.n_interpolations,
repeat=None,
queue_length=self.net_param.queue_length)
for _ in self.readers
])
# repeat each resized image n times, so that each
# image matches one random vector,
# (n = self.gan_param.n_interpolations)
self.sampler.append([
ResizeSampler(
reader=reader,
window_sizes=self.data_param,
batch_size=self.net_param.batch_size,
windows_per_image=self.gan_param.n_interpolations,
shuffle=False,
queue_length=self.net_param.queue_length)
for reader in self.readers
])