def main():
args = parse_args()
print('Called with args:')
print(args)
# Set main gpu
# theano.sandbox.cuda.use(args.gpu_id)
if args.cfg_files is not None:
for cfg_file in args.cfg_files:
cfg_from_file(cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
if not args.randomize:
np.random.seed(cfg.CONST.RNG_SEED)
if args.batch_size is not None:
cfg_from_list(['CONST.BATCH_SIZE', args.batch_size])
if args.iter is not None:
cfg_from_list(['TRAIN.NUM_ITERATION', args.iter])
if args.save_freq is not None:
cfg_from_list(['TRAIN.SAVE_FREQ', args.save_freq])
if args.valid_freq is not None:
cfg_from_list(['TRAIN.VALIDATION_FREQ', args.valid_freq])
if args.nan_check_freq is not None:
cfg_from_list(['TRAIN.NAN_CHECK_FREQ', args.nan_check_freq])
if args.net_name is not None:
cfg_from_list(['NET_NAME', args.net_name])
if args.model_name is not None:
cfg_from_list(['CONST.NETWORK_CLASS', args.model_name])
if args.dataset is not None:
cfg_from_list(['DATASET', args.dataset])
if args.exp is not None:
cfg_from_list(['TEST.EXP_NAME', args.exp])
if args.out_path is not None:
cfg_from_list(['DIR.OUT_PATH', args.out_path])
if args.weights is not None:
cfg_from_list([
'CONST.WEIGHTS', args.weights, 'TRAIN.RESUME_TRAIN', True,
'TRAIN.INITIAL_ITERATION',
int(args.init_iter)
])
print('Using config:')
pprint.pprint(cfg)
if not args.test:
train_net()
else:
test_net()
def main():
args = parse_args()
print('Called with args:')
print(args)
# Set main gpu
#theano.sandbox.cuda.use(args.gpu_id)
#theano.gpuarray.use(args.gpu_id)
if args.cfg_files is not None:
for cfg_file in args.cfg_files:
cfg_from_file(cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
if not args.randomize:
np.random.seed(cfg.CONST.RNG_SEED)
if args.batch_size is not None:
cfg_from_list(['CONST.BATCH_SIZE', args.batch_size])
if args.iter is not None:
cfg_from_list(['TRAIN.NUM_ITERATION', args.iter])
if args.net_name is not None:
cfg_from_list(['NET_NAME', args.net_name])
if args.model_name is not None:
cfg_from_list(['CONST.NETWORK_CLASS', args.model_name])
if args.dataset is not None:
cfg_from_list(['DATASET', args.dataset])
if args.exp is not None:
cfg_from_list(['TEST.EXP_NAME', args.exp])
if args.out_path is not None:
cfg_from_list(['DIR.OUT_PATH', args.out_path])
if args.tb_path is not None:
cfg_from_list(['DIR.TB_PATH', args.tb_path])
if args.dyna_dict is not None:
cfg_from_list(['CONST.dynamic_dict', args.dyna_dict])
if args.learn_rate is not None:
cfg_from_list(['TRAIN.DEFAULT_LEARNING_RATE', args.learn_rate])
if args.weights is not None:
cfg_from_list(['CONST.WEIGHTS', args.weights, 'TRAIN.RESUME_TRAIN', True,
'TRAIN.INITIAL_ITERATION', int(args.init_iter)])
print('Using config:')
pprint.pprint(cfg)
if not args.test:
train_net()
else:
test_net()