def test_name_mismatch(self):
with self.assertRaisesRegexp(KeyError, ""):
sampler = GridSampler(reader=get_dynamic_window_reader(),
data_param=MOD_2D_DATA,
batch_size=10,
spatial_window_size=None,
window_border=(0, 0, 0),
queue_length=10)
with self.assertRaisesRegexp(KeyError, ""):
sampler = GridSampler(reader=get_3d_reader(),
data_param=MOD_2D_DATA,
batch_size=10,
spatial_window_size=None,
window_border=(0, 0, 0),
queue_length=10)
def test_inverse_mapping(self):
reader = get_label_reader()
data_param = MOD_LABEL_DATA
sampler = GridSampler(reader=reader,
data_param=data_param,
batch_size=10,
spatial_window_size=None,
window_border=(3, 4, 5),
queue_length=50)
aggregator = GridSamplesAggregator(image_reader=reader,
name='label',
output_path=os.path.join(
'testing_data', 'aggregated'),
window_border=(3, 4, 5),
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()
output_data = nib.load(output_file).get_data()[..., 0, 0]
expected_data = nib.load(
'testing_data/T1_1023_NeuroMorph_Parcellation.nii.gz').get_data()
self.assertAllClose(output_data, expected_data)
def test_25d_init(self):
reader = get_25d_reader()
sampler = GridSampler(reader=reader,
data_param=SINGLE_25D_DATA,
batch_size=10,
spatial_window_size=None,
window_border=(3, 4, 5),
queue_length=50)
aggregator = GridSamplesAggregator(image_reader=reader,
name='image',
output_path=os.path.join(
'testing_data', 'aggregated'),
window_border=(3, 4, 5),
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['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 initialise_grid_sampler(self):
self.sampler = [[GridSampler(
reader=reader,
data_param=self.data_param,
batch_size=self.net_param.batch_size,
spatial_window_size=self.action_param.spatial_window_size,
window_border=self.action_param.border,
queue_length=self.net_param.queue_length) for reader in
self.readers]]
def test_3d_initialising(self):
sampler = GridSampler(reader=get_3d_reader(),
data_param=MULTI_MOD_DATA,
batch_size=10,
spatial_window_size=None,
window_border=(0, 0, 0),
queue_length=10)
with self.test_session() as sess:
coordinator = tf.train.Coordinator()
sampler.run_threads(sess, coordinator, num_threads=2)
out = sess.run(sampler.pop_batch_op())
self.assertAllClose(out['image'].shape, (10, 8, 10, 2, 2))
sampler.close_all()
def initialise_sampler(self):
if self.is_training:
self.sampler = [[UniformSampler(
reader=reader,
data_param=self.data_param,
batch_size=self.net_param.batch_size,
windows_per_image=self.action_param.sample_per_volume,
queue_length=self.net_param.queue_length) for reader in
self.readers]]
else:
self.sampler = [[GridSampler(
reader=reader,
data_param=self.data_param,
batch_size=self.net_param.batch_size,
spatial_window_size=self.action_param.spatial_window_size,
window_border=self.action_param.border,
queue_length=self.net_param.queue_length) for reader in
self.readers]]