def main(args):
# Parse config
cfg_file = args.cfg
with open(cfg_file, 'r') as f:
cfg = edict(yaml.load(f))
# Data generator
data_gen = DataProcTest(
face_img_dir=args.data_dir,
cache_path=args.cache_path,
sample_num=args.sample_num)
with tf.Session() as sess:
# Build network
reso_net = ResoNet(cfg=cfg, is_train=False)
reso_net.build()
# Build solver
solver = Solver(sess=sess, cfg=cfg, net=reso_net)
solver.init()
for i in range(data_gen.batch_num):
data = data_gen.get_batch(i, resize=cfg.IMG_SIZE[:2])
images = data['images']
name_list = data['name_list']
vis_im(images, 'tmp/vis.jpg')
prob = solver.test(images)
print(np.mean(prob[:, 0]))
def main(args):
# Parse config
cfg_file = args.cfg
with open(cfg_file, 'r') as f:
cfg = edict(yaml.load(f))
# Data generator
data_gen = DataProcTrain(
face_img_dir=args.data_dir,
cache_path=args.cache_path,
anno_path=args.list,
batch_size=cfg.TRAIN.BATCH_SIZE,
is_shuffle=True)
cfg.TRAIN.DECAY_STEP = data_gen.batch_num
epoch = cfg.TRAIN.NUM_EPOCH
with tf.Session() as sess:
# Build network
reso_net = ResoNet(cfg=cfg, is_train=True)
reso_net.build()
# Init solver
solver = Solver(sess=sess, cfg=cfg, net=reso_net)
solver.init()
# Train
count = 0
for epoch_id in range(epoch):
for i in range(data_gen.batch_num):
data = data_gen.get_batch(i, resize=cfg.IMG_SIZE[:2])
images = data['images']
labels = data['images_label']
ims_tmp = vis_im(images, 'tmp/vis.jpg')
summary, prob, net_loss, total_loss, weights = solver.train(images, labels)
pred_labels = np.argmax(prob, axis=1)
print('====================================')
print('Net loss: {}'.format(net_loss))
print('Total loss: {}'.format(total_loss))
print('Real label: {}'.format(np.array(labels)))
print('Pred label: {}'.format(pred_labels))
print('Neg hard mining: {}'.format(weights))
print('epoch: {}, batch_idx: {}'.format(epoch_id, i))
if count % 100 == 0:
solver.writer.add_summary(summary, count)
count += 1
if epoch_id % 2 == 0:
solver.save(epoch_id)
sample_num = 10
# Employ dlib to extract face area and landmark points
pwd = os.path.dirname(__file__)
# Fix to get it running on colab
cwd = os.getcwd()
front_face_detector = dlib.get_frontal_face_detector()
lmark_predictor = dlib.shape_predictor(
cwd + '/dlib_model/shape_predictor_68_face_landmarks.dat')
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# Build network
reso_net = ResoNet(cfg=cfg, is_train=False)
reso_net.build()
# Build solver
solver = Solver(sess=sess, cfg=cfg, net=reso_net)
solver.init()
def im_test(im):
face_info = lib.align(im[:, :, (2, 1, 0)], front_face_detector,
lmark_predictor)
# Samples
if len(face_info) == 0:
logging.warning('No faces are detected.')
prob = -1 # we ignore this case
else:
# Check how many faces in an image