def save(self, filename):
"""Saves the text map and serialized entities"""
folder = self.get_folder()
check_dir(folder)
file_path = folder + sep + filename
config = SafeConfigParser()
# Add the save header
s = self.__cfg_header
config.add_section(s)
config.set(s, "version", str(WORLD_VERSION))
# Serialize and save each entity
s = self.__cfg_entities
config.add_section(s)
for entity in self.entity_manager.get_entities().values():
uid = entity.get_uid()
data = self.entity_manager.serialize_entity(uid)
config.set(s, str(uid), ord_bytes(data))
# Add the map content
s = self.__cfg_map
config.add_section(s)
for x, y in enumerate(self.map):
config.set(s, str(x), y)
cfg_file = open(file_path, "w")
config.write(cfg_file)
cfg_file.close()
self.redraw_map = True
self.redraw_minimap = True
self.__log.info("Saved world %s" % filename)
def data_problem(self, data, info = "Problem with data"):
"""Warnings about problem with data"""
self.log.error("%s: %s" % (info, format_bytes(data)))
if data != None:
fail_folder = get_base_dir() + sep + UDP_FOLDER + sep
check_dir(fail_folder)
filename = fail_folder + str(round_int(time())) + ".udp"
fail = open(filename, "w")
fail.write(data)
fail.close()
def list(self):
"""Returns the maps in current selected folder"""
files = {}
files_names = []
files_full = []
folder = self.get_folder()
check_dir(folder)
for name in listdir(folder):
full = folder + sep + name
if isfile(full):
files[name] = full
files_names.append(name)
files_full.append(full)
return files, files_names, files_full
type=int,
default=5,
help='number of classes in one training task')
parser.add_argument('--save-path', default='/data/save_models/...')
parser.add_argument('--gpu', default='0')
parser.add_argument('--episodes-per-batch',
type=int,
default=8,
help='number of episodes per batch')
opt = parser.parse_args()
set_gpu(opt.gpu)
check_dir('./experiments/')
check_dir(opt.save_path)
log_file_path = os.path.join(opt.save_path, "train_log.txt")
log(log_file_path, str(vars(opt)))
(embedding_net, cls_head) = get_model(opt)
#------------------------------------------
dataset_train = tieredImageNet(phase='train')
dataset_val = tieredImageNet(phase='val')
data_loader = FewShotDataloader
dloader_train = data_loader(
dataset=dataset_train,
nKnovel=opt.train_way,