decode_predictions = config.decode_predictions
skip_pred_steps = config.skip_pred_steps
init_state_network = config.init_state_network
in_out_states = config.in_out_states
pred_gradient_loss = config.pred_gradient_loss
ls_prediction_loss = config.ls_prediction_loss
ls_supervision = config.ls_supervision
sqrd_diff_loss = config.sqrd_diff_loss
ls_split = config.ls_split
model_base_dir = find_model_base_dir(args.load_path)
data_args_path = None
if os.path.exists(os.path.join(model_base_dir, "data_args.txt")):
data_args_path = os.path.join(model_base_dir, "data_args.txt")
dataset_meta_info = read_args_file(data_args_path)
else:
data_args_path = os.path.join(config.data_path, "args.txt")
dataset_meta_info = read_args_file(data_args_path)
sup_param_count = max(
1,
int(dataset_meta_info['num_param']) -
2) # two parameters are always present -> scene num and frame num
res_x = int(dataset_meta_info["resolution_x"])
res_y = int(dataset_meta_info["resolution_y"])
res_z = int(dataset_meta_info["resolution_z"])
in_out_dim = 3 if "density" in config.data_type else 2
in_out_dim = in_out_dim + 1 if config.is_3d else in_out_dim
input_shape = (input_frame_count, )
# read config entries input_frame_count = config.input_frame_count prediction_window = config.w_num decode_predictions = config.decode_predictions skip_pred_steps = config.skip_pred_steps init_state_network = config.init_state_network in_out_states = config.in_out_states pred_gradient_loss = config.pred_gradient_loss ls_prediction_loss = config.ls_prediction_loss ls_supervision = config.ls_supervision sqrd_diff_loss = config.sqrd_diff_loss ls_split = config.ls_split train_prediction_only = config.train_prediction_only dataset_meta_info = read_args_file(os.path.join(config.data_path, 'args.txt')) sup_param_count = max(1,int(dataset_meta_info['num_param']) - 2) # two parameters are always present -> scene num and frame num res_x = int(dataset_meta_info["resolution_x"]) res_y = int(dataset_meta_info["resolution_y"]) res_z = int(dataset_meta_info["resolution_z"]) in_out_dim = 3 if "levelset" in config.data_type else 2 in_out_dim = in_out_dim + 1 if config.is_3d else in_out_dim input_shape = (input_frame_count,) input_shape += (res_z,) if config.is_3d else () input_shape += (res_y, res_x, in_out_dim) if classic_ae: rec_pred = RecursivePredictionCleanSplit(config=config, input_shape=input_shape, decode_predictions=decode_predictions, skip_pred_steps=skip_pred_steps, init_state_network=init_state_network, in_out_states=in_out_states, pred_gradient_loss=pred_gradient_loss, ls_prediction_loss=ls_prediction_loss, ls_supervision=ls_supervision, sqrd_diff_loss=sqrd_diff_loss, ls_split=ls_split, supervised_parameters=sup_param_count, train_prediction_only=train_prediction_only) else: rec_pred = RecursivePrediction(config=config, input_shape=input_shape, decode_predictions=decode_predictions, skip_pred_steps=skip_pred_steps, init_state_network=init_state_network, in_out_states=in_out_states, pred_gradient_loss=pred_gradient_loss, ls_prediction_loss=ls_prediction_loss, ls_supervision=ls_supervision, sqrd_diff_loss=sqrd_diff_loss, ls_split=ls_split, supervised_parameters=sup_param_count, train_prediction_only=train_prediction_only)
def initialize_networks(args, config, norm_factors):
net = type('net', (), {})()
net.norm_factors = norm_factors
# read dataset meta information from data set directory
net.dataset_meta_info = read_args_file(
os.path.join(config.data_path, 'args.txt'))
# extract important properties for model creation
net.sup_param_count = max(
1,
int(net.dataset_meta_info['num_param']) -
2) # two parameters are always present -> scene num and frame num
net.res_x = int(net.dataset_meta_info["resolution_x"])
net.res_y = int(net.dataset_meta_info["resolution_y"])
net.res_z = int(net.dataset_meta_info["resolution_z"])
# prepare input shape
in_out_dim = 3 if "density" in config.data_type else 2
in_out_dim = in_out_dim + 1 if config.is_3d else in_out_dim
net.input_shape = (config.input_frame_count, )
net.input_shape += (net.res_z, ) if config.is_3d else ()
net.input_shape += (net.res_y, net.res_x, in_out_dim)
# create models
net.classic_ae = args.classic_ae
net.is_3d = net.res_z > 1
if net.classic_ae:
net.rec_pred = RecursivePredictionCleanSplit(
config=config,
input_shape=net.input_shape,
decode_predictions=config.decode_predictions,
skip_pred_steps=config.skip_pred_steps,
init_state_network=config.init_state_network,
in_out_states=config.in_out_states,
pred_gradient_loss=config.pred_gradient_loss,
ls_prediction_loss=config.ls_prediction_loss,
ls_supervision=config.ls_supervision,
sqrd_diff_loss=config.sqrd_diff_loss,
ls_split=config.ls_split,
supervised_parameters=net.sup_param_count)
else:
net.rec_pred = RecursivePrediction(
config=config,
input_shape=net.input_shape,
decode_predictions=config.decode_predictions,
skip_pred_steps=config.skip_pred_steps,
init_state_network=config.init_state_network,
in_out_states=config.in_out_states,
pred_gradient_loss=config.pred_gradient_loss,
ls_prediction_loss=config.ls_prediction_loss,
ls_supervision=config.ls_supervision,
sqrd_diff_loss=config.sqrd_diff_loss,
ls_split=config.ls_split,
supervised_parameters=net.sup_param_count)
# load weights from file
net.rec_pred.load_model(args.load_path)
# create separate prediction model and copy over weights
net.pred = Prediction(config=net.rec_pred.config,
input_shape=(net.rec_pred.w_num, net.rec_pred.z_num))
net.pred._build_model()
net.pred.model.set_weights(net.rec_pred.pred.model.get_weights())
# create prediction history
net.prediction_history = PredictionHistory(
in_ts=net.rec_pred.w_num, data_shape=(net.rec_pred.z_num, ))
return net