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))