def test_close_early(self):
sampler = UniformSampler(reader=get_dynamic_window_reader(),
window_sizes=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(),
window_sizes=DYNAMIC_MOD_DATA,
batch_size=2,
windows_per_image=10,
queue_length=10)
sampler.close_all()
def test_ill_init(self):
with self.assertRaisesRegexp(ValueError, ""):
sampler = UniformSampler(reader=get_3d_reader(),
window_sizes=MOD_2D_DATA,
batch_size=2,
windows_per_image=10,
queue_length=10)
def __init__(self,
reader,
window_sizes,
batch_size=1,
windows_per_image=1,
queue_length=10,
name='balanced_sampler'):
UniformSampler.__init__(self,
reader=reader,
window_sizes=window_sizes,
batch_size=batch_size,
windows_per_image=windows_per_image,
queue_length=queue_length,
name=name)
tf.logging.info('Initialised balanced sampler window instance')
self.window_centers_sampler = balanced_spatial_coordinates
def __init__(self,
reader,
window_sizes,
batch_size=1,
windows_per_image=1,
queue_length=10,
name='weighted_sampler'):
UniformSampler.__init__(self,
reader=reader,
window_sizes=window_sizes,
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 test_dynamic_init(self):
sampler = UniformSampler(reader=get_dynamic_window_reader(),
window_sizes=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(),
window_sizes=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_3d_concentric_init(self):
sampler = UniformSampler(reader=get_concentric_window_reader(),
window_sizes=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(),
window_sizes=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()