def load_net(modelname):
modelfile = os.path.join(cfg.SNAPSHOT_DIR, modelname)
meta = read_meta(modelfile)
input_size = meta.get('input_size', 256)
output_size = meta.get('output_size', input_size)
z_dim = meta.get('z_dim', 99)
net = AAE(input_size=input_size, output_size=output_size, z_dim=z_dim)
print("Loading model {}...".format(modelfile))
read_model(modelfile, 'saae', net)
print("Model trained for {} iterations.".format(meta['total_iter']))
return net
def load_net(model, num_landmarks=None):
meta = nn.read_meta(model)
input_size = meta.get('input_size', 256)
output_size = meta.get('output_size', input_size)
if num_landmarks is None:
num_landmarks = 98
num_landmarks = meta.get('num_landmarks', num_landmarks)
z_dim = meta.get('z_dim', 99)
net = Fabrec(num_landarks=num_landmarks,
input_size=input_size,
output_size=output_size,
z_dim=z_dim)
print("Loading model {}...".format(model))
nn.read_model(model, 'saae', net)
return net
def _load_snapshot(self, snapshot_name, data_dir=None):
if data_dir is None:
data_dir = self.snapshot_dir
model_snap_dir = os.path.join(data_dir, snapshot_name)
try:
nn.read_model(model_snap_dir, 'saae', self.net)
except KeyError as e:
print(e)
meta = nn.read_meta(model_snap_dir)
self.epoch = meta['epoch']
self.total_iter = meta['total_iter']
self.total_training_time_previous = meta.get('total_time', 0)
self.total_images = meta.get('total_images', 0)
self.best_score = meta['best_score']
self.net.total_iter = self.total_iter
str_training_time = str(datetime.timedelta(seconds=self.total_training_time()))
log.info("Model {} trained for {} iterations ({}).".format(snapshot_name, self.total_iter, str_training_time))