def do_experiment(model_config):
tf.reset_default_graph()
experiment_id = ex.current_run._id
print('Experiment ID: {eid}'.format(eid=experiment_id))
# Prepare data
print('Preparing dataset')
train_data, val_data, test_data = dataset.prepare_datasets(model_config)
print('Dataset ready')
# Start session
tf_config = tf.ConfigProto()
#tf_config.gpu_options.allow_growth = True
tf_config.gpu_options.visible_device_list = str(model_config['GPU'])
sess = tf.Session(config=tf_config)
#sess = tf.Session()
#sess = tf_debug.LocalCLIDebugWrapperSession(sess, ui_type="readline")
print('Session started')
# Create iterators
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(handle,
train_data.output_types,
train_data.output_shapes)
mixed_spec, voice_spec, background_spec, mixed_audio, voice_audio, background_audio = iterator.get_next(
)
training_iterator = train_data.make_initializable_iterator()
validation_iterator = val_data.make_initializable_iterator()
testing_iterator = test_data.make_initializable_iterator()
training_handle = sess.run(training_iterator.string_handle())
validation_handle = sess.run(validation_iterator.string_handle())
testing_handle = sess.run(testing_iterator.string_handle())
print('Iterators created')
# Create variable placeholders and model
is_training = tf.placeholder(shape=(), dtype=bool)
mixed_phase = tf.expand_dims(mixed_spec[:, :, :-1, 3], 3)
print('Creating model')
if model_config['data_type'] == 'mag':
mixed_input = tf.expand_dims(mixed_spec[:, :, :-1, 2], 3)
voice_input = tf.expand_dims(voice_spec[:, :, :-1, 2], 3)
elif model_config['data_type'] in ['mag_phase', 'mag_phase_diff']:
mixed_input = mixed_spec[:, :, :-1, 2:4]
voice_input = voice_spec[:, :, :-1, 2:4]
elif model_config['data_type'] == 'real_imag':
mixed_input = mixed_spec[:, :, :-1, 0:2]
voice_input = voice_spec[:, :, :-1, 0:2]
elif model_config['data_type'] in ['mag_real_imag', 'mag_phase2']:
mixed_input = tf.concat([
tf.expand_dims(mixed_spec[:, :, :-1, 2], 3), mixed_spec[:, :, :-1,
0:2]
], 3)
voice_input = tf.concat([
tf.expand_dims(voice_spec[:, :, :-1, 2], 3), voice_spec[:, :, :-1,
0:2]
], 3)
elif model_config['data_type'] == 'mag_phase_real_imag':
mixed_input = mixed_spec[:, :, :-1, :]
voice_input = voice_spec[:, :, :-1, :]
model = audio_models.MagnitudeModel(mixed_input,
voice_input,
mixed_phase,
mixed_audio,
voice_audio,
background_audio,
model_config['model_variant'],
is_training,
model_config['learning_rate'],
model_config['data_type'],
model_config['phase_weight'],
name='Magnitude_Model')
sess.run(tf.global_variables_initializer())
if model_config['loading']:
print('Loading checkpoint')
checkpoint = os.path.join(model_config['model_base_dir'],
model_config['checkpoint_to_load'])
restorer = tf.train.Saver()
restorer.restore(sess, checkpoint)
# Summaries
model_folder = str(experiment_id)
writer = tf.summary.FileWriter(os.path.join(model_config["log_dir"],
model_folder),
graph=sess.graph)
# Get baseline metrics at initialisation
test_count = 0
if model_config['initialisation_test']:
print('Running initialisation test')
initial_test_loss, test_count = test(sess, model, model_config, handle,
testing_iterator, testing_handle,
test_count, experiment_id)
# Train the model
model = train(sess, model, model_config, model_folder, handle,
training_iterator, training_handle, validation_iterator,
validation_handle, writer)
# Test trained model
mean_test_loss, test_count = test(sess, model, model_config, handle,
testing_iterator, testing_handle,
test_count, experiment_id)
print('{ts}:\n\tAll done with experiment {exid}!'.format(
ts=datetime.datetime.now(), exid=experiment_id))
if model_config['initialisation_test']:
print('\tInitial test loss: {init}'.format(init=initial_test_loss))
print('\tFinal test loss: {final}'.format(final=mean_test_loss))
def do_experiment(model_config):
tf.reset_default_graph()
experiment_id = ex.current_run._id
print('Experiment ID: {eid}'.format(eid=experiment_id))
# Prepare data
print('Preparing dataset')
train_data, val_data, test_data = dataset.prepare_datasets(model_config)
print('Dataset ready')
# Start session
#tf_config = tf.ConfigProto()
#tf_config.gpu_options.allow_growth = True
#tf_config.gpu_options.visible_device_list = "0"
#sess = tf.Session(config=tf_config)
sess = tf.Session()
print('Session started')
# Create iterators
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(handle, train_data.output_types, train_data.output_shapes)
mixed_spec, voice_spec, mixed_audio, voice_audio = iterator.get_next()
training_iterator = train_data.make_initializable_iterator()
validation_iterator = val_data.make_initializable_iterator()
testing_iterator = test_data.make_initializable_iterator()
training_handle = sess.run(training_iterator.string_handle())
validation_handle = sess.run(validation_iterator.string_handle())
testing_handle = sess.run(testing_iterator.string_handle())
print('Iterators created')
# Create variable placeholders and model
is_training = tf.placeholder(shape=(), dtype=bool)
if model_config['mag_phase']:
mixed_mag = tf.expand_dims(mixed_spec[:, :, :-1, 0], 3)
mixed_phase = tf.expand_dims(mixed_spec[:, :, :-1, 1], 3)
voice_mag = tf.expand_dims(voice_spec[:, :, :-1, 0], 3)
print('Creating model')
model = audio_models.MagnitudeModel(mixed_mag, voice_mag, mixed_phase, mixed_audio, voice_audio,
model_config['model_variant'], is_training, name='U_Net_Model')
else:
mixed_spec_trim = mixed_spec[:, :, :-1, :]
voice_spec_trim = voice_spec[:, :, :-1, :]
print('Creating model')
model = audio_models.ComplexNumberModel(mixed_spec_trim, voice_spec_trim, mixed_audio, voice_audio,
model_config['model_variant'], is_training)
sess.run(tf.global_variables_initializer())
if model_config['loading']:
# TODO - Think this works now but needs proper testing
print('Loading checkpoint')
checkpoint = os.path.join(model_config['model_base_dir'], model_config['checkpoint_to_load'])
restorer = tf.train.Saver()
restorer.restore(sess, checkpoint)
# Summaries
model_folder = str(experiment_id)
writer = tf.summary.FileWriter(os.path.join(model_config["log_dir"], model_folder), graph=sess.graph)
# Get baseline metrics at initialisation
test_count = 0
if model_config['initialisation_test']:
print('Running initialisation test')
initial_test_loss, test_count = test(sess, model, model_config, handle, testing_iterator, testing_handle,
writer, test_count, experiment_id)
# Train the model
model = train(sess, model, model_config, model_folder, handle, training_iterator, training_handle,
validation_iterator, validation_handle, writer)
# Test trained model
mean_test_loss, test_count = test(sess, model, model_config, handle, testing_iterator, testing_handle, writer,
test_count, experiment_id)
print('{ts}:\n\tAll done with experiment {exid}!'.format(ts=datetime.datetime.now(), exid=experiment_id))
if model_config['initialisation_test']:
print('\tInitial test loss: {init}'.format(init=initial_test_loss))
print('\tFinal test loss: {final}'.format(final=mean_test_loss))
