def __init__(self,
model_file='./model.h5',
elm_model_files=None,
feat_path='./temp.csv',
context_len=5,
max_time_steps=300,
elm_hidden_num=50,
stl=True,
elm_main_task_id=-1,
sr=16000,
tasks='arousal:2,valence:2'):
self.stl = stl
self.model = self.model = keras.models.load_model(model_file)
self.elm_model_files = elm_model_files
self.sess = tf.Session()
self.elm_model = []
self.tasks = []
self.tasks_names = []
self.total_high_level_feat = 0
#setting multi-task
for task in tasks.split(","):
print("task: ", task)
task_n_class = task.split(':')
self.tasks.append(int(task_n_class[1]))
self.tasks_names.append(task_n_class[0])
self.total_high_level_feat = self.total_high_level_feat + int(
task_n_class[1])
#seeting an elm model for a post-classifier
if self.elm_model_files != None:
print("elm model is loaded")
elm_tasks = elm_model_files.split(',')
if len(elm_tasks) == len(self.tasks):
print("#tasks: ", len(self.tasks))
for i in range(0, len(self.tasks)):
elm_model_task = ELM(self.sess,
1,
self.total_high_level_feat * 4,
elm_hidden_num,
self.tasks[i],
task=self.tasks_names[i])
elm_path = elm_tasks[i]
elm_model_task.load(elm_path)
self.elm_model.append(elm_model_task)
self.elm_hidden_num = elm_hidden_num
self.elm_main_task_id = elm_main_task_id
else:
print("mismatch between tasks and elm models")
exit()
self.sr = sr
self.feat_path = feat_path
self.context_len = context_len
self.max_time_steps = max_time_steps
self.model.summary()
def __init__(self, model_file = './model.h5', elm_model_files = None, feat_path = './temp.csv', context_len = 5, max_time_steps = 300, elm_hidden_num = 50, stl = True, elm_main_task_id = -1, sr = 16000, tasks = 'arousal:2,valence:2', min_max = None, seq2seq = False):
self.stl = stl
self.model = self.model = keras.models.load_model(model_file, custom_objects={'Conv3DHighway': Conv3DHighway, 'Conv2DHighway': Conv2DHighway, 'Conv1DHighway': Conv1DHighway, 'Highway': Highway, 'w_categorical_crossentropy': w_categorical_crossentropy, 'categorical_focal_loss': categorical_focal_loss, 'f1': f1, 'precision': precision, 'recall': recall})
self.seq2seq = seq2seq
self.elm_model_files = elm_model_files
self.sess = tf.Session()
self.elm_model = []
self.tasks = []
self.tasks_names = []
self.total_high_level_feat = 0
self.feat_ext = FeatExt(min_max)
#print("Plotting model")
#config = self.model.get_config()
#print(config)
#plot_model(self.model, to_file='model.png', show_shapes=True)
#setting multi-task
for task in tasks.split(","):
print("task: ", task)
task_n_class = task.split(':')
self.tasks.append(int(task_n_class[1]))
self.tasks_names.append(task_n_class[0])
self.total_high_level_feat = self.total_high_level_feat + int(task_n_class[1])
#seeting an elm model for a post-classifier
if self.elm_model_files != None:
print("elm model is loaded")
elm_tasks = elm_model_files.split(',')
if len(elm_tasks) == len(self.tasks):
print("#tasks: ", len(self.tasks))
for i in range(0, len(self.tasks)):
elm_model_task = ELM(self.sess, 1, self.total_high_level_feat * 4, elm_hidden_num, self.tasks[i], task = self.tasks_names[i])
elm_path = elm_tasks[i]
elm_model_task.load(elm_path)
self.elm_model.append(elm_model_task)
self.elm_hidden_num = elm_hidden_num
self.elm_main_task_id = elm_main_task_id
else:
print("mismatch between tasks and elm models")
exit()
self.sr = sr
self.feat_path = feat_path
self.context_len = context_len
self.max_time_steps = max_time_steps
def elm_load_predict(model,
X_test,
multiTasks,
unweighted,
stl,
dictForLabelsTest,
hidden_num=50,
main_task_id=-1,
elm_load_path='./model/elm.ckpt',
dataset='test'):
sess = tf.Session()
print('elm high level feature generating')
pred_test = model.predict([X_test])
feat_test = high_level_feature_mtl(pred_test,
stl=stl,
main_task_id=main_task_id)
print('high level feature dim: ', feat_test.shape[1])
scores = []
for task, classes, idx in multiTasks:
elm = ELM(sess, feat_test.shape[0], feat_test.shape[1], hidden_num,
dictForLabelsTest[task].shape[1])
print('elm loading')
elm.load(elm_load_path)
print('elm testing')
if unweighted:
labels = dictForLabelsTest[task]
preds = elm.test(feat_test)
scores.append(unweighted_recall(preds, labels, task, dataset))
else:
acc = elm.test(feat_test, labels)
scores.append(acc)
return scores
