def initialise_sampler(self):
self.sampler = []
if self.is_training:
self.sampler.append([
ResizeSampler(reader=reader,
data_param=self.data_param,
batch_size=self.net_param.batch_size,
windows_per_image=1,
shuffle_buffer=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,
data_param=self.data_param,
batch_size=self.net_param.batch_size,
windows_per_image=1,
shuffle_buffer=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,
data_param=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=self.reader,
data_param=self.data_param,
batch_size=self.net_param.batch_size,
windows_per_image=1,
shuffle_buffer=True,
queue_length=self.net_param.queue_length))
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))
# 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=self.reader,
data_param=self.data_param,
batch_size=self.net_param.batch_size,
windows_per_image=self.gan_param.n_interpolations,
shuffle_buffer=False,
queue_length=self.net_param.queue_length))
def initialise_resize_sampler(self):
self.sampler = [[ResizeSampler(
reader=reader,
data_param=self.data_param,
batch_size=self.net_param.batch_size,
shuffle_buffer=self.is_training,
queue_length=self.net_param.queue_length) for reader in
self.readers]]
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,
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_3d_init(self):
sampler = ResizeSampler(reader=get_3d_reader(),
data_param=MULTI_MOD_DATA,
batch_size=1,
shuffle_buffer=False,
queue_length=1)
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, [1, 7, 10, 2, 2])
sampler.close_all()
def test_3d_init(self):
sampler = ResizeSampler(
reader=get_3d_reader(),
data_param=MULTI_MOD_DATA,
batch_size=1,
shuffle_buffer=False,
queue_length=1)
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, [1, 7, 10, 2, 2])
sampler.close_all()