def main(args):
# Combine the default config with
# the external config file and the set command
if args.cfg is not None:
cfg_from_file(args.cfg)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
cfg.DEBUG = args.debug
cfg.GPU_ID = args.gpu_id
cfg_print(cfg)
# Loading the network
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
net = caffe.Net(args.prototxt, args.model, caffe.TEST)
# Create the imdb
imdb = get_imdb(args.db_name)
# Set the network name
net.name = args.net_name
# Evaluate the network
test_net(net,
imdb,
visualize=args.visualize,
no_cache=args.no_cache,
output_path=args.out_path)
def main(args):
# Combine the default config with
# the external config file and the set command
if args.cfg is not None:
cfg_from_file(args.cfg)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
cfg.DEBUG = args.debug
cfg.GPU_ID = args.gpu_id
cfg_print(cfg)
# Loading the network
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
net = caffe.Net(args.prototxt, args.model, caffe.TEST)
# Create the imdb
imdb = get_imdb(args.db_name)
# Set the network name
net.name = args.net_name
# Evaluate the network
test_net(net, imdb, visualize=args.visualize, no_cache=args.no_cache, output_path=args.out_path)
default=None,
nargs=argparse.REMAINDER)
return parser.parse_args()
if __name__ == '__main__':
args = parser()
# Load settings
if args.conf_file:
cfg_from_file(args.conf_file)
# For train and test, usually we do not need cache; unless overridden by amend
cfg.TEST.NO_CACHE = True
if args.set_cfgs:
cfg_from_list(args.set_cfgs)
# Record logs into cfg
cfg.LOG.CMD = ' '.join(sys.argv)
cfg.LOG.TIME = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
np.random.seed(int(cfg.RNG_SEED))
if cfg.TENSORBOARD.ENABLE:
tb.client = Tensorboard(hostname=cfg.TENSORBOARD.HOSTNAME,
port=cfg.TENSORBOARD.PORT)
tb.sess = tb.client.create_experiment(cfg.NAME + '_' + cfg.LOG.TIME)
if args.train == 'true' or args.train == 'True': # the training entrance
# Get training imdb
imdb = get_imdb(cfg.TRAIN.DB)
roidb = get_training_roidb(imdb)
default='SSH/configs/wider.yml', type=str)
parser.add_argument('--iters', dest='iters', help='Number of iterations for training the network',
default=21000, type=int)
return parser.parse_args()
if __name__ == '__main__':
# Get command line arguments
args = parser()
# Combine external configs with SSH default configs
if args.cfg is not None:
cfg_from_file(args.cfg)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
cfg_print(cfg,test=False)
# Set the GPU ids
gpu_list = args.gpu_ids.split(',')
gpus = [int(i) for i in gpu_list]
# Set the random seed for numpy
np.random.seed(cfg.RNG_SEED)
# Prepare the training roidb
imdb= get_imdb(args.db_name)
roidb = get_training_roidb(imdb)
# Train the model
train_net(args.solver_proto, roidb, output_dir=get_output_dir(imdb.name),