num_class = args.num_class
if len(args.class_names) > 0:
if os.path.isfile(args.class_names):
# try to open it to read class names
with open(args.class_names, 'r') as f:
class_names = [l.strip() for l in f.readlines()]
else:
class_names = [c.strip() for c in args.class_names.split(',')]
assert len(class_names) == num_class
for name in class_names:
assert len(name) > 0
else:
class_names = None
network = None if args.deploy_net else args.network
evaluate_net(network,
args.rec_path,
num_class, (args.mean_r, args.mean_g, args.mean_b),
args.data_shape,
args.prefix,
args.epoch,
ctx,
batch_size=args.batch_size,
path_imglist=args.list_path,
nms_thresh=args.nms_thresh,
force_nms=args.force_nms,
ovp_thresh=args.overlap_thresh,
use_difficult=args.use_difficult,
class_names=class_names,
voc07_metric=args.use_voc07_metric)
ctx = mx.cpu()
else:
ctx = [mx.gpu(int(i)) for i in args.gpu_id.split(',')]
# parse # classes and class_names if applicable
num_class = args.num_class
if len(args.class_names) > 0:
if os.path.isfile(args.class_names):
# try to open it to read class names
with open(args.class_names, 'r') as f:
class_names = [l.strip() for l in f.readlines()]
else:
class_names = [c.strip() for c in args.class_names.split(',')]
assert len(class_names) == num_class
for name in class_names:
assert len(name) > 0
else:
class_names = None
network = None if args.deploy_net else args.network
if args.prefix.endswith('_'):
prefix = args.prefix + args.network
else:
prefix = args.prefix
evaluate_net(network, args.rec_path, num_class,
(args.mean_r, args.mean_g, args.mean_b), args.data_shape,
prefix, args.epoch, ctx, batch_size=args.batch_size,
path_imglist=args.list_path, nms_thresh=args.nms_thresh,
force_nms=args.force_nms, ovp_thresh=args.overlap_thresh,
use_difficult=args.use_difficult, class_names=class_names,
voc07_metric=args.use_voc07_metric)
parser.add_argument('--gpus', dest='gpu_id', help='GPU devices to evaluate with',
default='0', type=str)
parser.add_argument('--cpu', dest='cpu', help='use cpu to evaluate',
action='store_true')
parser.add_argument('--data-shape', dest='data_shape', type=int, default=300,
help='set image shape')
parser.add_argument('--mean-r', dest='mean_r', type=float, default=123,
help='red mean value')
parser.add_argument('--mean-g', dest='mean_g', type=float, default=117,
help='green mean value')
parser.add_argument('--mean-b', dest='mean_b', type=float, default=104,
help='blue mean value')
parser.add_argument('--nms', dest='nms_thresh', type=float, default=0.45,
help='non-maximum suppression threshold')
parser.add_argument('--force', dest='force_nms', type=bool, default=False,
help='force non-maximum suppression on different class')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
if args.cpu:
ctx = mx.cpu()
else:
ctx = [mx.gpu(int(i)) for i in args.gpu_id.split(',')]
evaluate_net(args.network, args.dataset, args.devkit_path,
(args.mean_r, args.mean_g, args.mean_b), args.data_shape,
args.prefix, args.epoch, ctx, year=args.year,
sets=args.eval_set, batch_size=args.batch_size,
nms_thresh=args.nms_thresh, force_nms=args.force_nms)
network1 = None if args.deploy_net else args.network1
if args.prefix1.endswith('_'):
prefix1 = args.prefix1 + args.network1
else:
prefix1 = args.prefix1
evaluate_net(network,
args.rec_path,
num_class, (args.mean_r, args.mean_g, args.mean_b),
args.data_shape,
prefix,
args.epoch,
ctx,
batch_size=args.batch_size,
path_imglist=args.list_path,
nms_thresh=args.nms_thresh,
force_nms=args.force_nms,
ovp_thresh=args.overlap_thresh,
use_difficult=args.use_difficult,
class_names=class_names,
voc07_metric=args.use_voc07_metric,
use_second_network=use_sub_network,
net1=network1,
path_imgrec1=args.rec_path1,
epoch1=args.epoch1,
model_prefix1=args.prefix1,
data_shape1=args.data_shape1)
else:
# single network
evaluate_net(network,
args.rec_path,
num_class, (args.mean_r, args.mean_g, args.mean_b),
