def init():
util.lock()
util.set_img_format()
util.override_keras_directory_iterator_next()
util.set_classes_from_train_dir()
util.set_samples_info()
if not os.path.exists(config.trained_dir):
os.mkdir(config.trained_dir)
def init():
util.lock()#detect previous precess has completed or not
util.set_img_format()#set image format: channels first or channels last
util.override_keras_directory_iterator_next()
util.set_classes_from_train_dir()#data_dir: data/sorted/train/
util.set_samples_info()
if util.get_keras_backend_name() != 'theano':
util.tf_allow_growth()
if not os.path.exists(config.trained_dir):
os.mkdir(config.trained_dir)
def init():
util.lock()
util.set_img_format()
util.override_keras_directory_iterator_next()
util.set_classes_from_train_dir()
util.set_samples_info()
if util.get_keras_backend_name() != 'theano':
util.tf_allow_growth()
if not os.path.exists(config.trained_dir):
os.mkdir(config.trained_dir)
def init():
util.lock()
util.set_img_format()
util.override_keras_directory_iterator_next()
util.set_classes_from_train_dir()
util.set_samples_info()
if util.get_keras_backend_name() != 'theano':
util.tf_allow_growth()
if not os.path.exists(config.trained_dir):
os.mkdir(config.trained_dir)
def init():
util.lock()
util.set_img_format()
# Pay extremely attention to the RGB->BGR, the next method is override.
util.override_keras_directory_iterator_next()
util.set_classes_from_train_dir()
util.set_samples_info()
if util.get_keras_backend_name() != 'theano':
util.tf_allow_growth()
if not os.path.exists(config.trained_dir):
os.mkdir(config.trained_dir)
if __name__ == '__main__':
try:
args = parse_args()
if args.data_dir:
config.data_dir = args.data_dir
config.set_paths()
if args.model:
config.model = args.model
util.lock()
util.override_keras_directory_iterator_next()
util.set_classes_from_train_dir()
util.set_samples_info()
if not os.path.exists(config.trained_dir):
os.mkdir(config.trained_dir)
class_weight = util.get_class_weight(config.train_dir)
# TODO: create class instance without dynamic module import
model = util.get_model_class_instance(
class_weight=class_weight,
nb_epoch=args.nb_epoch,
freeze_layers_number=args.freeze_layers_number)
model.train()
print('Training is finished!')
except (KeyboardInterrupt, SystemExit):
util.unlock()
except Exception as e:
print(e)
