def test_SUBFUNCTIONS_prepare_MULTIPROCESSING(self): ds = dict() for i in range(0, 5): img = np.random.rand(16, 16, 16) * 255 img = img.astype(int) seg = np.random.rand(16, 16, 16) * 3 seg = seg.astype(int) sample = (img, seg) ds["TEST.sample_" + str(i)] = sample io_interface = Dictionary_interface(ds, classes=3, three_dim=True) self.tmp_dir = tempfile.TemporaryDirectory(prefix="tmp.miscnn.") tmp_batches = os.path.join(self.tmp_dir.name, "batches") dataio = Data_IO(io_interface, input_path="", output_path="", batch_path=tmp_batches, delete_batchDir=False) sf = [Resize((8,8,8)), Normalization(), Clipping(min=-1.0, max=0.0)] pp = Preprocessor(dataio, batch_size=1, prepare_subfunctions=True, analysis="fullimage", subfunctions=sf, use_multiprocessing=True) pp.mp_threads = 4 sample_list = dataio.get_indiceslist() pp.run_subfunctions(sample_list, training=True) batches = pp.run(sample_list, training=True, validation=False) self.assertEqual(len(os.listdir(tmp_batches)), 5) for i in range(0, 5): file_prepared_subfunctions = os.path.join(tmp_batches, str(pp.data_io.seed) + ".TEST.sample_" + str(i) + ".pickle") self.assertTrue(os.path.exists(file_prepared_subfunctions)) img = batches[i][0] seg = batches[i][1] self.assertIsNotNone(img) self.assertIsNotNone(seg) self.assertEqual(img.shape, (1,8,8,8,1)) self.assertEqual(seg.shape, (1,8,8,8,3))
def test_PREPROCESSOR_BASE_prepareSubfunctions(self): sample_list = self.data_io3D.get_indiceslist() pp = Preprocessor(self.data_io3D, batch_size=1, analysis="fullimage", prepare_subfunctions=True) pp.run_subfunctions(sample_list[0:8], training=True) pp.run(sample_list[0:8], training=True, validation=False) pp.run_subfunctions(sample_list[8:10], training=False) pp.run(sample_list[8:10], training=False, validation=False)