def test(img_path, anno_path):
# load net
num_classes = len(labelmap) + 1 # +1 for background
net = build_ssd('test', 300, num_classes) # initialize SSD
net.load_state_dict(
torch.load(args.trained_model, map_location=torch.device('cpu')))
net.eval()
print('Finished loading model!')
# load data
dataset = SIXrayDetection(args.dataset_root,
args.dataset,
BaseTransform(300, dataset_mean),
SIXrayAnnotationTransform(),
test_set_path=(img_path, anno_path))
if args.cuda:
net = net.cuda()
cudnn.benchmark = True
# evaluation
test_net(args.result_folder,
net,
args.cuda,
dataset,
BaseTransform(net.size, dataset_mean),
args.top_k,
300,
thresh=args.confidence_threshold)
def test(img_path, anno_path):
# load net
num_classes = len(SIXray_CLASSES) + 1 # +1 background
net = build_ssd('test', 300, num_classes) # initialize SSD
net.load_state_dict(torch.load(args.trained_model, map_location=torch.device('cpu')))
net.eval()
# read and put into a file
test_sets = []
for anno_file in os.listdir(anno_path):
test_sets.append(anno_file.split('.')[0])
testset = SIXrayDetection(test_sets, None, SIXrayAnnotationTransform(), image_path=img_path, anno_path=anno_path)
test_net(args.save_folder, net, args.cuda, testset,
BaseTransform(net.size, (104, 117, 123)),
thresh=args.visual_threshold)
def test_voc():
# load net
num_classes = len(SIXray_CLASSES) + 1 # +1 background
net = build_ssd('test', 300, num_classes) # initialize SSD
net.load_state_dict(torch.load(args.trained_model,map_location=torch.device('cpu')))
net.eval()
print('Finished loading model!')
# load data
test_sets = "./data/sixray/test_1650.txt"
testset = SIXrayDetection(test_sets, None, SIXrayAnnotationTransform())
if args.cuda:
net = net.cuda()
cudnn.benchmark = True
# evaluation
test_net(args.save_folder, net, args.cuda, testset,
BaseTransform(net.size, (104, 117, 123)),
thresh=args.visual_threshold)
def test_voc():
# load net
num_classes = len(SIXray_CLASSES) + 1 # +1 background
net = build_ssd('test', 300, num_classes) # initialize SSD
net.load_state_dict(torch.load(args.trained_model))
net.eval()
print('Finished loading model!')
# load data
testset = SIXrayDetection(args.sixray_root, [('core_3000', 'val'),
('coreless_3000', 'val')],
None, SIXrayAnnotationTransform())
if args.cuda:
net = net.cuda()
cudnn.benchmark = True
# evaluation
test_net(args.save_folder,
net,
args.cuda,
testset,
BaseTransform(net.size, (104, 117, 123)),
thresh=args.visual_threshold)
def evaluate_detections(box_list, output_dir, dataset):
write_voc_results_file(box_list, dataset)
do_python_eval(output_dir)
if __name__ == '__main__':
# load net
num_classes = len(labelmap) + 1 # +1 for background
net = build_ssd('test', 300, num_classes) # initialize SSD
net.load_state_dict(torch.load(args.trained_model))
net.eval()
print('Finished loading model!')
# load data
dataset = SIXrayDetection(args.sixray_root, args.imagesetfile,
BaseTransform(300, dataset_mean),
SIXrayAnnotationTransform())
if args.cuda:
net = net.cuda()
cudnn.benchmark = True
# evaluation
test_net(args.save_folder,
net,
args.cuda,
dataset,
BaseTransform(net.size, dataset_mean),
args.top_k,
300,
thresh=args.confidence_threshold)