imdb, roidb, ratio_list, ratio_index = combined_roidb(args.imdbtest_name, training = False)
imdb.competition_mode(on=True)
print('{:d} roidb entries'.format(len(roidb)))
input_dir = args.load_dir + "/" + args.net + "/" + args.dataset
if not os.path.exists(input_dir):
raise Exception('There is no input directory for loading network from ' + input_dir)
load_name = os.path.join(input_dir,
'faster_rcnn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint))
# initilize the network here.
if args.net == 'vgg16':
fasterRCNN = vgg16(imdb.classes, pretrained=False, class_agnostic=args.class_agnostic)
elif args.net == 'vgg16_4ch':
fasterRCNN = vgg16_4ch(imdb.classes, pretrained=True, class_agnostic=args.class_agnostic)
elif args.net == 'vgg16_5ch':
fasterRCNN = vgg16_5ch(imdb.classes, pretrained=True, class_agnostic=args.class_agnostic)
elif args.net == 'res101':
fasterRCNN = resnet(imdb.classes, 101, pretrained=False, class_agnostic=args.class_agnostic)
elif args.net == 'res50':
fasterRCNN = resnet(imdb.classes, 50, pretrained=False, class_agnostic=args.class_agnostic)
elif args.net == 'res152':
fasterRCNN = resnet(imdb.classes, 152, pretrained=False, class_agnostic=args.class_agnostic)
else:
print("network is not defined")
pdb.set_trace()
# initilize the tensor holder here.
im_data = torch.FloatTensor(1)
'''it = 0
for model in models:
if it 20:
load_name = os.path.join(input_dir,model)
it += 1'''
detection_classes = np.asarray(['__background__', 'Poma'])
# initilize the network here.
if args.net == 'vgg16':
fasterRCNN = vgg16(detection_classes,
pretrained=False,
class_agnostic=args.class_agnostic)
elif args.net == 'vgg16_4ch':
fasterRCNN = vgg16_4ch(detection_classes,
pretrained=False,
class_agnostic=args.class_agnostic)
elif args.net == 'vgg16_5ch':
fasterRCNN = vgg16_5ch(detection_classes,
pretrained=False,
class_agnostic=args.class_agnostic)
elif args.net == 'res101':
fasterRCNN = resnet(detection_classes,
101,
pretrained=False,
class_agnostic=args.class_agnostic)
elif args.net == 'res50':
fasterRCNN = resnet(detection_classes,
50,
pretrained=False,
class_agnostic=args.class_agnostic)