required=True)
parser.add_argument(
'-g',
'--generation_mode',
help=
'Mode of image generation. Required for any training environment with "CNN" inside their name!',
required=False,
default=None)
parser.add_argument('-n',
'--model_name',
help='Name of the RL model',
required=True)
args = parser.parse_args()
computer = ut.getComputer(args.computer)
env_name = ut.getImEnv(args.environment)
gen_mode = ut.getGenMode(args.generation_mode)
model_name = ut.getModelName(args.model_name)
config = ut.loadYAMLFromFile('config_' + env_name + '.yaml')
### RL paths (model)
use_rl_env_path = os.path.join(config['paths'][computer]['rl'], model_name)
print('use_rl_env_path: {}'.format(use_rl_env_path))
# Building config_path
config_path = {}
config_path['critic_model_path'] = os.path.join(use_rl_env_path,
'critic_model.pth')
required=True)
parser.add_argument('-e',
'--environment',
help='Environment from which the images are created',
required=True)
parser.add_argument('-g',
'--generation_mode',
help='Mode of generation',
required=True)
parser.add_argument('-t',
'--test_only',
help='If only test',
action='store_true')
args = parser.parse_args()
computer = utils.getComputer(args.computer)
environment = utils.getImEnv(args.environment)
gen_mode = utils.getGenMode(args.generation_mode)
# Loading config
config = utils.loadYAMLFromFile('config_' + environment + '.yaml')
img_gen = image_generator(config, environment, gen_mode)
nb_train_images = config['exp']['nb_train_im']
nb_test_images = config['exp']['nb_test_im']
# Generating images
if not args.test_only:
img_gen.create_images(nb_train_images, 'train', computer, config,
gen_mode)
img_gen.create_images(nb_test_images, 'test', computer, config, gen_mode)