def _read_optimizer(config_data) -> Optimizer:
optimizer_config: Dict[str, Any] = config_data['optimizer']
optimizer_name = list(optimizer_config.keys())[0]
assert optimizer_name in Configuration.KNOWN_OPTIMIZERS, f'Unknown optimizer: {optimizer_name}'
if optimizer_name == 'adam':
return Adam.from_config(optimizer_config[optimizer_name])
elif optimizer_name == 'sgd':
return SGD.from_config(optimizer_config[optimizer_name])
ls['out_normals'] = SNMT.loss_nmap(conf['kappa'])
lsw['out_normals'] = conf['w_normals']
if use_dmap:
ls['out_depth_maps'] = SNMT.loss_dmap()
lsw['out_depth_maps'] = conf['w_depth']
if use_pc:
ls['out_verts'] = SNMT.loss_pcloud()
lsw['out_verts'] = conf['w_coords']
if args.model_state:
snmt.model.load_weights(args.model_state, by_name=True)
if args.optim_state:
with open(args.optim_state, 'rb') as f:
opt_state = pickle.load(f)
optimizer = Adam.from_config(opt_state['config'])
snmt.model.compile(optimizer, loss=ls, loss_weights=lsw)
snmt.model._make_train_function()
optimizer.set_weights(opt_state['params'])
ep_start = helpers.extract_epoch(args.model_state)
else:
optimizer = Adam(lr=conf['lr'])
snmt.model.compile(optimizer, loss=ls, loss_weights=lsw)
ep_start = 1
# LR scheduler, early stopping.
redlr = None
earlstop = None
if conf['red_lr_plateau']:
redlr = ReduceLROnPlateau(monitor='loss_va',
factor=conf['red_lr_factor'],
