def main(_):
args = parser.parse_args()
log.init("FaceNeural", logging.DEBUG, log_path="output/log.txt")
with tf.Session() as sess:
if args.phase == "train":
model = Face(sess, args)
model.train(args)
log.info('train mode')
elif args.phase == "inference":
log.info("inference")
model = Face(sess, args)
model.inference(args)
elif args.phase == "lightcnn":
log.info("light cnn test")
elif args.phase == "faceparsing":
log.info("faceparsing")
elif args.phase == "net":
log.info("net start with ports (%d, %d)", 5010, 5011)
net = Net(5010, 5011)
while True:
r_input = raw_input("command: \n")
if r_input == "s":
msg = raw_input("input: ")
net.only_send(msg)
elif r_input == 'r':
msg = raw_input("input: ")
net.send_recv(msg)
elif r_input == "q":
net.only_send("quit")
net.close()
break
else:
log.error("unknown code, quit")
net.close()
break
if support_gpu and arguments.use_gpu:
if not arguments.gpuid:
arguments.gpuid = 0
dev = torch.device("cuda:%d" % arguments.gpuid)
return True, dev
else:
dev = torch.device("cpu")
return False, dev
if __name__ == '__main__':
"""
程序入口函数
"""
args = parser.parse_args()
log.init("FaceNeural", logging.INFO, log_path="./output/neural_log.txt")
cuda, device = init_device(args)
if args.phase == "train_imitator":
log.info('imitator train mode')
imitator = Imitator("neural imitator", args)
if cuda:
imitator.cuda()
imitator.batch_train(cuda)
elif args.phase == "train_extractor":
log.info('feature extractor train mode')
extractor = Extractor("neural extractor", args)
if cuda:
extractor.cuda()
extractor.batch_train(cuda)
elif args.phase == "inference_imitator":
cv2.imwrite(path_save.replace("align_", "align2_"), scaled)
return scaled
except Exception as e:
log.error(e)
def clean(path):
for root, dirs, files in os.walk(path):
for file in files:
if file.startswith("align"):
path = os.path.join(root, file)
os.remove(path)
def export(path):
for root, dirs, files in os.walk(path):
for file in files:
path1 = os.path.join(root, file)
path2 = os.path.join(root, "align_" + file)
face_features(path1, path2)
if __name__ == '__main__':
log.init("align")
pwd = os.getcwd()
project_path = os.path.abspath(os.path.dirname(pwd) + os.path.sep + ".")
model_path = os.path.join(project_path, "neural/output/image/")
log.info(model_path)
clean(model_path)
export(model_path)
"""
count = len(self.losses)
if count > 0:
plt.style.use('seaborn-whitegrid')
x = range(count)
y1 = []
y2 = []
for it in self.losses:
y1.append(it[0])
y2.append(it[1])
plt.plot(x, y1, color='r', label='l1')
plt.plot(x, y2, color='g', label='l2')
plt.ylabel("loss")
plt.xlabel('step')
plt.legend()
path = os.path.join(self.prev_path, "loss.png")
plt.savefig(path)
plt.close('all')
if __name__ == '__main__':
import logging
from parse import parser
log.info("evaluation mode start")
args = parser.parse_args()
log.init("FaceNeural", logging.INFO, log_path="./output/evaluate.txt")
evl = Evaluate(args, cuda=torch.cuda.is_available())
img = cv2.imread(args.eval_image).astype(np.float32)
evl.itr_train(img)