# Generator
mp = maps[chosen_map]
# Chooses map for generation
# Simulator
config.load_simulator = sim_start
config.simulator_graphics = False
config.simulator_initial_map = mp
config.simulator_algorithm_type, config.simulator_testing_type, config.simulator_algorithm_parameters = algo
config.simulator_key_frame_speed, config.simulator_key_frame_skip = ani
config.simulator_write_debug_level = debug
# These are for training
config.generator_labelling_features = labelling[training_algo][0]
config.generator_labelling_labels = labelling[training_algo][1]
config.generator_single_labelling_features = labelling[training_algo][2]
config.generator_single_labelling_labels = labelling[training_algo][3]
config.generator_aug_labelling_features = []
config.generator_aug_labelling_labels = []
config.generator_aug_single_labelling_features = []
config.generator_aug_single_labelling_labels = []
config.generator_modify = None
config.generator_show_gen_sample = show_sample_map
config.generator_nr_of_examples = nbr_ex
if args.full_train:
config.generator = True
for m in gen_maps.values():
config.generator_gen_type = m
#Generator
config.generator = gen_start
if config.generator_house_expo:
gen_map = '_house_expo'
config.generator_labelling_atlases = [gen_map]
config.generator_nr_of_examples = nbr_ex
else:
gen_map = gen_maps[chosen_map]
config.generator_labelling_atlases = [gen_map + '_' + str(nbr_ex)]
config.generator_nr_of_examples = nbr_ex
config.generator_gen_type = gen_map
#These are for training
config.generator_labelling_features = []
config.generator_labelling_labels = []
config.generator_single_labelling_features = []
config.generator_single_labelling_labels = []
config.generator_aug_labelling_features = []
config.generator_aug_labelling_labels = []
config.generator_aug_single_labelling_features = []
config.generator_aug_single_labelling_labels = []
config.generator_modify = None
config.generator_show_gen_sample = show_sample_map
#Trainer
config.trainer = train_start
# config.trainer_model = training_algo #Either BasicLSTMModule or CAE or LSTMCAEModel
# config.trainer_custom_config = {
# "local_kernel": (AStar, ([], {})),