class Pretrained_Inference(Trainer):
def __init__(self, separator, name, **kwargs):
super(Pretrained_Inference, self).__init__(trainer_type=name, **kwargs)
self.separator = separator
def build(self):
self.args.update({'pretraining':True})
self.model = Adapt(**self.args)
self.model.create_saver()
self.model.restore_model(self.args['model_folder'])
config_model["type"] = "DPCL_finetuning"
learning_rate = 0.001
batch_size = 2
config_model["chunk_size"] = chunk_size
config_model["alpha"] = learning_rate
config_model["batch_size"] = batch_size
folder = 'DPCL_finetuning'
model = Adapt(config_model=config_model, pretraining=False)
model.create_saver()
path = os.path.join(config.model_root, 'log', 'DPCL_train_front')
model.restore_model(path, full_id)
model.connect_front_back_to_separator(DPCL)
with model.graph.as_default():
model.create_saver()
model.restore_model(path, full_id)
# model.freeze_front()
model.optimize
model.tensorboard_init()
init = model.non_initialized_variables()
model.sess.run(init)
print 'Total name :'
####
config_model["type"] = "L41_finetuning"
learning_rate = 0.001
batch_size = 64
config_model["chunk_size"] = chunk_size
config_model["alpha"] = learning_rate
config_model["batch_size"] = batch_size
model = Adapt(config_model=config_model, pretraining=False)
with model.graph.as_default():
model.connect_front(L41Model)
var_list = [v for v in tf.global_variables() if ('front' in v.name)]
model.create_saver(var_list)
model.restore_model(path_adapt, full_id_adapt)
model.sepNet.prediction
model.sepNet.separate
model.sepNet.output = model.sepNet.enhance
var_list = [
v for v in tf.global_variables()
if ('prediction' in v.name or 'speaker_centroids' in v.name
or 'enhance' in v.name)
]
model.create_saver(var_list)
model.restore_model(path, full_id)
model.separator
model.back
var_list = [v for v in tf.global_variables() if ('back/' in v.name)]
model.create_saver(var_list)
model.restore_model(path_adapt, full_id_adapt)