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 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()