def __init__(self,
face_rec_graph,
model_path='models/model-20170512-110547.ckpt-250000'):
'''
:param face_rec_sess: FaceRecSession object
:param model_path:
'''
print("Loading Resnet Model")
with face_rec_graph.graph.as_default():
self.sess = tf.Session()
self.x = tf.placeholder('float', [None, 160, 160, 3],
name='batch_join')
#default input for the NN is 160x160x3
self.embeddings = tf.nn.l2_normalize(resnet.inference(
self.x, 0.6, phase_train=False)[0],
1,
1e-10,
name='embeddings')
#some magic numbers that u dont have to care about
saver = tf.train.Saver() #saver load pretrain model
saver.restore(self.sess, model_path)
print("Resnet Model loaded")
def __init__(self, face_rec_graph, model_path = 'models/model-20170512-110547.ckpt-250000'):
print("Loading model...")
with face_rec_graph.as_default():
self.sess = tf.Session()
self.x = tf.placeholder('float', [None,160,160,3]);
self.embeddings = tf.nn.l2_normalize(
resnet.inference(self.x, 0.6, phase_train=False)[0], 1, 1e-10);
saver = tf.train.Saver() #load pretrain model
saver.restore(self.sess, model_path)
print("Model loaded")
def __init__(self, model_path = 'models/model-20170512-110547.ckpt-250000'):
'''
:param face_rec_sess: FaceRecSession object
:param model_path:
'''
print("Loading face recognition models(it will take about 1 minute)...")
with tf.Graph().as_default(), tf.device('/cpu:0'):
self.sess = tf.Session()
self.x = tf.placeholder('float', [None,160,160,3]); #default input for the NN is 160x160x3
self.embeddings = tf.nn.l2_normalize(
resnet.inference(self.x, 0.6, phase_train=False)[0], 1, 1e-10);
saver = tf.train.Saver() #saver load pretrain model
saver.restore(self.sess, model_path)
print("Face recognition models loaded")
def __init__(self, face_rec_graph, model_path = 'models/model-20170512-110547.ckpt-250000'):
'''
:param face_rec_sess: FaceRecSession object
:param model_path:
'''
print("Loading model...")
with face_rec_graph.graph.as_default():
self.sess = tf.Session()
self.x = tf.placeholder('float', [None,160,160,3]); #default input for the NN is 160x160x3
self.embeddings = tf.nn.l2_normalize(
resnet.inference(self.x, 0.6, phase_train=False)[0], 1, 1e-10); #some magic numbers that u dont have to care about
saver = tf.train.Saver() #saver load pretrain model
saver.restore(self.sess, model_path)
print("Model loaded")
def __init__(self, model_path='models/model-20170512-110547.ckpt-250000'):
'''
:param face_rec_sess: FaceRecSession object
:param model_path:
'''
print(
"Loading face recognition models(it will take about 1 minute)...")
with tf.Graph().as_default(), tf.device('/cpu:0'):
self.sess = tf.Session()
self.x = tf.placeholder('float', [None, 160, 160, 3])
#default input for the NN is 160x160x3
self.embeddings = tf.nn.l2_normalize(
resnet.inference(self.x, 0.6, phase_train=False)[0], 1, 1e-10)
saver = tf.train.Saver() #saver load pretrain model
saver.restore(self.sess, model_path)
print("Face recognition models loaded")
def __init__(self,
face_rec_graph,
model_path='models/model-20170512-110547.ckpt-250000'):
print("\n [INFO] Loading the pretrained model...")
with face_rec_graph.graph.as_default():
self.sess = tf.Session()
# Default input shape -> [160, 160, 3]
self.x = tf.placeholder('float', [None, 160, 160, 3])
# Reload the base model variables
self.embeddings = tf.nn.l2_normalize(
resnet.inference(self.x,
KEEP_PROB,
phase_train=DO_NOT_TRAIN_MODEL)[0], 1, 1e-10)
# Restore the pretrained model
saver = tf.train.Saver()
saver.restore(self.sess, model_path)
print("\n [INFO] Sucessfully loaded model.")
def __init__(self,
face_rec_graph,
save_part,
model_path='models/model-20170512-110547.ckpt-250000'):
'''
:param face_rec_sess: FaceRecSession object
:param model_path:
'''
if save_part == True:
print("Loading model...")
with face_rec_graph.graph.as_default():
self.sess = tf.Session() #face_rec_graph.sess #
self.sess.run(tf.global_variables_initializer())
self.x = tf.placeholder('float', [None, 160, 160, 3],
name='input')
#default input for the NN is 160x160x3
self.embeddings = tf.nn.l2_normalize(
resnet.inference(self.x, 0.6, phase_train=False)[0], 1,
1e-10)
#some magic numbers that u dont have to care about
print("self.embeddings")
print(self.embeddings)
saver = tf.train.Saver() #saver load pretrain model
saver.restore(self.sess, model_path)
print("Model loaded")
writer = tf.summary.FileWriter("/tmp/model4/FaceFeature/",
self.sess.graph)
saver = tf.train.Saver() #saver load pretrain model
save_path = tf.train.Saver().save(
self.sess, "/tmp/model4/FaceFeature/model.ckpt")
print("Model saved in path: %s" % save_path)
self.features = lambda img: self.sess.run(
('l2_normalize:0'), feed_dict={"input:0": img})
else:
if model_pb == True:
print("Load FaceFeature from saved pb file ")
model_filename = '/tmp/model4/FaceFeature/graph/graph_FaceFeature.pb'
g_in = load_graph(model_filename)
self.sess = tf.Session(graph=g_in)
# self.sess.run(tf.global_variables_initializer())
print("///////////////////llllll")
for v in tf.trainable_variables():
# if v.name == scope_variable:
print(v)
print("/******************/")
print(self.sess.run(v))
for v in self.sess.graph.get_operations():
print(v)
print("/******************/")
# print(self.sess.run(v))
else:
print("Load FaceFeature from saved model ")
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
model_path = "/tmp/model4/FaceFeature/model.ckpt"
model_path_p = "/tmp/model4/FaceFeature/"
new_saver = tf.train.import_meta_graph(model_path + '.meta')
new_saver.restore(self.sess,
tf.train.latest_checkpoint(model_path_p))
self.features = lambda img: self.sess.run(
('prefix/l2_normalize:0'), feed_dict={"prefix/input:0": img})
