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 test_init(self):
sampler = LinearInterpolateSampler(reader=get_3d_reader(),
data_param=MULTI_MOD_DATA,
batch_size=1,
n_interpolations=8,
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, 256, 168, 256, 2])
sampler.close_all()
def test_init(self):
sampler = LinearInterpolateSampler(
reader=get_3d_reader(),
data_param=MULTI_MOD_DATA,
batch_size=1,
n_interpolations=8,
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, 256, 168, 256, 2])
sampler.close_all()