def test_close_early(self):
sampler = UniformSampler(reader=get_dynamic_window_reader(),
data_param=DYNAMIC_MOD_DATA,
batch_size=2,
windows_per_image=10,
queue_length=10)
sampler.close_all()
def test_close_early(self):
sampler = UniformSampler(reader=get_dynamic_window_reader(),
data_param=DYNAMIC_MOD_DATA,
batch_size=2,
windows_per_image=10,
queue_length=10)
sampler.close_all()
def __init__(self,
reader,
data_param,
batch_size,
windows_per_image,
queue_length=10):
UniformSampler.__init__(self,
reader=reader,
data_param=data_param,
batch_size=batch_size,
windows_per_image=windows_per_image,
queue_length=queue_length)
tf.logging.info('Initialised balanced sampler window instance')
self.window_centers_sampler = balanced_spatial_coordinates
def __init__(self,
reader,
data_param,
batch_size,
windows_per_image,
queue_length=10):
UniformSampler.__init__(self,
reader=reader,
data_param=data_param,
batch_size=batch_size,
windows_per_image=windows_per_image,
queue_length=queue_length)
tf.logging.info('Initialised weighted sampler window instance')
self.spatial_coordinates_generator = weighted_spatial_coordinates
def test_ill_init(self):
with self.assertRaisesRegexp(KeyError, ""):
sampler = UniformSampler(reader=get_3d_reader(),
data_param=MOD_2D_DATA,
batch_size=2,
windows_per_image=10,
queue_length=10)
def __init__(self,
reader,
data_param,
batch_size,
windows_per_image,
queue_length=10,
name='weighted_sampler'):
UniformSampler.__init__(self,
reader=reader,
data_param=data_param,
batch_size=batch_size,
windows_per_image=windows_per_image,
queue_length=queue_length,
name=name)
tf.logging.info('Initialised weighted sampler window instance')
self.window_centers_sampler = weighted_spatial_coordinates
def initialise_uniform_sampler(self):
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]]
def __init__(self,
reader,
data_param,
batch_size,
windows_per_image,
constraint,
random_windows_per_image=0,
queue_length=10):
UniformSampler.__init__(self,
reader=reader,
data_param=data_param,
batch_size=batch_size,
windows_per_image=windows_per_image,
queue_length=queue_length)
self.constraint = constraint
self.n_samples_rand = random_windows_per_image
self.spatial_coordinates_generator = \
self.selective_spatial_coordinates()
tf.logging.info('initialised selective sampling')
def __init__(self,
reader,
data_param,
batch_size,
windows_per_image,
constraint,
random_windows_per_image=0,
queue_length=10):
UniformSampler.__init__(self,
reader=reader,
data_param=data_param,
batch_size=batch_size,
windows_per_image=windows_per_image,
queue_length=queue_length)
self.constraint = constraint
self.n_samples_rand = random_windows_per_image
self.spatial_coordinates_generator = \
self.selective_spatial_coordinates()
tf.logging.info('initialised selective sampling')
def test_dynamic_init(self):
sampler = UniformSampler(reader=get_dynamic_window_reader(),
data_param=DYNAMIC_MOD_DATA,
batch_size=2,
windows_per_image=10,
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, (1, 8, 2, 256, 2))
sampler.close_all()
def test_dynamic_init(self):
sampler = UniformSampler(reader=get_dynamic_window_reader(),
data_param=DYNAMIC_MOD_DATA,
batch_size=2,
windows_per_image=10,
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, (1, 8, 2, 256, 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]]
def test_3d_concentric_init(self):
sampler = UniformSampler(reader=get_concentric_window_reader(),
data_param=MULTI_WINDOW_DATA,
batch_size=2,
windows_per_image=10,
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())
img_loc = out['image_location']
seg_loc = out['label_location']
self.assertTrue(np.all(img_loc[:, 0] == seg_loc[:, 0]))
self.assertTrue(np.all((img_loc - seg_loc)[:, 1:4] == [1, 1, 0]))
self.assertTrue(np.all((img_loc - seg_loc)[:, 4:] == [-2, -1, 1]))
self.assertAllClose(out['image'].shape, (2, 4, 10, 3, 1))
self.assertAllClose(out['label'].shape, (2, 7, 12, 2, 1))
sampler.close_all()
def test_3d_concentric_init(self):
sampler = UniformSampler(reader=get_concentric_window_reader(),
data_param=MULTI_WINDOW_DATA,
batch_size=2,
windows_per_image=10,
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())
img_loc = out['image_location']
seg_loc = out['label_location']
self.assertTrue(np.all(img_loc[:, 0] == seg_loc[:, 0]))
self.assertTrue(np.all((img_loc - seg_loc)[:, 1:4] == [1, 1, 0]))
self.assertTrue(np.all((img_loc - seg_loc)[:, 4:] == [-2, -1, 1]))
self.assertAllClose(out['image'].shape, (2, 4, 10, 3, 1))
self.assertAllClose(out['label'].shape, (2, 7, 12, 2, 1))
sampler.close_all()
