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()