def run():
args = parse_args()
# use cuda
if args.cuda:
device = torch.device("cuda")
else:
device = torch.device("cpu")
input_size = [args.input_size, args.input_size]
# load net
if args.version == 'centernet':
from models.centernet import CenterNet
net = CenterNet(device,
input_size=input_size,
num_classes=80,
conf_thresh=args.conf_thresh,
nms_thresh=args.nms_thresh,
use_nms=args.use_nms)
net.load_state_dict(torch.load(args.trained_model, map_location='cuda'))
net.to(device).eval()
print('Finished loading model!')
# run
if args.mode == 'camera':
detect(net, device, BaseTransform(net.input_size),
thresh=args.visual_threshold, mode=args.mode)
elif args.mode == 'image':
detect(net, device, BaseTransform(net.input_size),
thresh=args.visual_threshold, mode=args.mode, path_to_img=args.path_to_img)
elif args.mode == 'video':
detect(net, device, BaseTransform(net.input_size),
thresh=args.visual_threshold, mode=args.mode, path_to_vid=args.path_to_vid, path_to_save=args.path_to_saveVid)
def test():
# get device
if args.cuda:
print('use cuda')
cudnn.benchmark = True
device = torch.device("cuda")
else:
device = torch.device("cpu")
# load net
num_classes = len(VOC_CLASSES)
testset = VOCDetection(args.voc_root, [('2007', 'test')], None,
VOCAnnotationTransform())
cfg = config.voc_cfg
if args.version == 'centernet':
from models.centernet import CenterNet
net = CenterNet(device,
input_size=cfg['min_dim'],
num_classes=num_classes)
net.load_state_dict(torch.load(args.trained_model, map_location=device))
net.to(device).eval()
print('Finished loading model!')
# evaluation
test_net(net,
device,
testset,
BaseTransform(net.input_size,
mean=(0.406, 0.456, 0.485),
std=(0.225, 0.224, 0.229)),
thresh=args.visual_threshold)
def run():
args = parse_args()
if args.cuda:
device = torch.device("cuda")
else:
device = torch.device("cpu")
if args.setup == 'VOC':
print('use VOC style')
cfg = config.voc_cfg
num_classes = 20
elif args.setup == 'COCO':
print('use COCO style')
cfg = config.coco_cfg
num_classes = 80
else:
print('Only support VOC and COCO !!!')
exit(0)
if args.version == 'centernet':
from models.centernet import CenterNet
net = CenterNet(device, input_size=cfg['min_dim'], num_classes=num_classes, use_nms=True)
net.load_state_dict(torch.load(args.trained_model, map_location=device))
net.to(device).eval()
print('Finished loading model!')
# run
if args.mode == 'camera':
detect(net, device, BaseTransform(net.input_size, mean=(0.406, 0.456, 0.485), std=(0.225, 0.224, 0.229)),
thresh=args.vis_thresh, mode=args.mode, setup=args.setup)
elif args.mode == 'image':
detect(net, device, BaseTransform(net.input_size, mean=(0.406, 0.456, 0.485), std=(0.225, 0.224, 0.229)),
thresh=args.vis_thresh, mode=args.mode, path_to_img=args.path_to_img, setup=args.setup)
elif args.mode == 'video':
detect(net, device, BaseTransform(net.input_size, mean=(0.406, 0.456, 0.485), std=(0.225, 0.224, 0.229)),
thresh=args.vis_thresh, mode=args.mode, path_to_vid=args.path_to_vid, path_to_save=args.path_to_saveVid, setup=args.setup)
def test():
# get device
if args.cuda:
print('use cuda')
cudnn.benchmark = True
device = torch.device("cuda")
else:
device = torch.device("cpu")
# load net
num_classes = 80
if args.dataset == 'COCO':
cfg = config.coco_cfg
testset = COCODataset(
data_dir=args.dataset_root,
json_file='instances_val2017.json',
name='val2017',
img_size=cfg['min_dim'][0],
debug=args.debug)
elif args.dataset == 'VOC':
cfg = config.voc_cfg
testset = VOCDetection(VOC_ROOT, [('2007', 'test')], None, VOCAnnotationTransform())
if args.version == 'centernet':
from models.centernet import CenterNet
net = CenterNet(device, input_size=cfg['min_dim'], num_classes=num_classes)
net.load_state_dict(torch.load(args.trained_model, map_location='cuda'))
net.to(device).eval()
print('Finished loading model!')
# evaluation
test_net(net, device, testset,
BaseTransform(net.input_size, mean=(0.406, 0.456, 0.485), std=(0.225, 0.224, 0.229)),
thresh=args.visual_threshold)
device = torch.device("cuda")
else:
device = torch.device("cpu")
# input size
input_size = [args.input_size, args.input_size]
# load net
if args.version == 'centernet':
from models.centernet import CenterNet
net = CenterNet(device,
input_size=input_size,
num_classes=num_classes,
backbone=args.backbone,
use_nms=args.use_nms)
# load net
net.load_state_dict(torch.load(args.trained_model, map_location='cuda'))
net.eval()
print('Finished loading model!')
net = net.to(device)
# evaluation
with torch.no_grad():
if args.dataset == 'voc':
voc_test(net, device, input_size)
elif args.dataset == 'coco-val':
coco_test(net, device, input_size, test=False)
elif args.dataset == 'coco-test':
coco_test(net, device, input_size, test=True)
name='val2017',
img_size=input_size[0])
class_colors = [(np.random.randint(255), np.random.randint(255),
np.random.randint(255)) for _ in range(num_classes)]
# load net
if args.version == 'centernet':
from models.centernet import CenterNet
net = CenterNet(device,
input_size=input_size,
num_classes=num_classes,
conf_thresh=args.conf_thresh,
nms_thresh=args.nms_thresh,
use_nms=args.use_nms)
net.load_state_dict(torch.load(args.trained_model, map_location=device))
net.to(device).eval()
print('Finished loading model!')
# evaluation
test(net=net,
device=device,
testset=dataset,
transform=BaseTransform(input_size),
thresh=args.visual_threshold,
class_colors=class_colors,
class_names=class_names,
class_indexs=class_indexs,
dataset=args.dataset)
