def test_save_load(self):
dir_out = self._get_test_dir()
theta_start = 0.01
theta_stop = 0.055
theta_step = 0.005
thetas = np.arange(theta_start, theta_stop, theta_step)
k = 6
frame_size_ms = 5
atom_gen = AtomLabelGen(self.dir_wcad_root, dir_out, thetas, k,
frame_size_ms)
label_dict, *extracted_norm_params = atom_gen.gen_data(
self.dir_wav, dir_out, id_list=self.id_list, return_dict=True)
# Call this once before starting the pre-processing.
norm_params = atom_gen.get_normalisation_params(dir_out)
self.assertTrue((extracted_norm_params[0] == norm_params[0]).all())
self.assertTrue((extracted_norm_params[1] == norm_params[1]).all())
test_label = label_dict[self.id_list[1]]
test_label_pre = atom_gen.preprocess_sample(test_label)
self.assertTrue(
np.isclose(test_label_pre, atom_gen[self.id_list[1]]).all())
test_label_post = atom_gen.postprocess_sample(test_label_pre)
# Post-precessing does peak selection, so pre and post labels
# are not the same and we cannot check for equality here.
self.assertTrue(np.isclose(-0.2547, test_label_post.sum(),
atol=0.0001))
os.remove(os.path.join(self.dir_database, "wcad_.txt"))
shutil.rmtree(dir_out)
def __init__(self,
wcad_root,
dir_atom_labels,
dir_question_labels,
id_list,
thetas,
k,
num_questions,
hparams=None):
"""Default constructor.
:param wcad_root: Path to main directory of wcad.
:param dir_atom_labels: Path to directory that contains the .atom files.
:param dir_question_labels: Path to directory that contains the .questions files.
:param id_list: List containing all ids. Subset is taken as test set.
:param thetas: List of theta values.
:param k: K value of atoms.
:param num_questions: Expected number of questions in question labels.
:param hparams: Hyper-parameter container.
"""
if hparams is None:
hparams = self.create_hparams()
hparams.out_dir = os.path.curdir
# Write missing default parameters.
if hparams.variable_sequence_length_train is None:
hparams.variable_sequence_length_train = hparams.batch_size_train > 1
if hparams.variable_sequence_length_test is None:
hparams.variable_sequence_length_test = hparams.batch_size_test > 1
if hparams.synth_dir is None:
hparams.synth_dir = os.path.join(hparams.out_dir, "synth")
# If the weight for unvoiced frames is not given, compute it to get equal weights.
non_zero_occurrence = min(0.99, 0.02 / len(thetas))
zero_occurrence = 1 - non_zero_occurrence
if not hasattr(hparams, "weight_zero"):
hparams.add_hparam("weight_non_zero", 1 / non_zero_occurrence)
hparams.add_hparam("weight_zero", 1 / zero_occurrence)
elif hparams.weight_zero is None:
hparams.weight_non_zero = 1 / non_zero_occurrence
hparams.weight_zero = 1 / zero_occurrence
super().__init__(id_list, hparams)
self.InputGen = QuestionLabelGen(dir_question_labels, num_questions)
self.InputGen.get_normalisation_params(
dir_question_labels, hparams.input_norm_params_file_prefix)
self.OutputGen = AtomLabelGen(wcad_root, dir_atom_labels, thetas, k,
hparams.frame_size_ms)
self.OutputGen.get_normalisation_params(
dir_atom_labels, hparams.output_norm_params_file_prefix)
self.dataset_train = PyTorchLabelGensDataset(self.id_list_train,
self.InputGen,
self.OutputGen,
hparams,
match_lengths=True)
self.dataset_val = PyTorchLabelGensDataset(self.id_list_val,
self.InputGen,
self.OutputGen,
hparams,
match_lengths=True)
if self.loss_function is None:
self.loss_function = WeightedNonzeroMSELoss(
hparams.use_gpu,
hparams.weight_zero,
hparams.weight_non_zero,
size_average=False,
reduce=False)
if hparams.scheduler_type == "default":
hparams.scheduler_type = "Plateau"
hparams.add_hparams(plateau_patience=10,
plateau_factor=0.5,
plateau_verbose=True)