# 'sheep', 'sofa', 'train', 'tvmonitor']) classes = np.asarray(['__background__', 'diabete']) if args.cascade: if args.net == 'detnet59': fpn = detnet_cascade(classes, 59, pretrained=False, class_agnostic=args.class_agnostic) else: print("network is not defined") pdb.set_trace() else: if args.net == 'detnet59': fpn = detnet_noncascade(classes, 59, pretrained=False, class_agnostic=args.class_agnostic) else: print("network is not defined") pdb.set_trace() fpn.create_architecture() # checkpoint = torch.load(load_name) # fpn.load_state_dict(checkpoint['model']) checkpoint = torch.load(load_name) fpn.load_state_dict({ k: v for k, v in checkpoint['model'].items() if k in fpn.state_dict() })
cfg.CUDA = True # initilize the network here. if args.cascade: if args.net == 'detnet59': FPN = detnet_cascade(imdb.classes, 59, pretrained=True, class_agnostic=args.class_agnostic) else: print("network is not defined") pdb.set_trace() else: if args.net == 'detnet59': FPN = detnet_noncascade(imdb.classes, 59, pretrained=True, class_agnostic=args.class_agnostic) else: print("network is not defined") pdb.set_trace() FPN.create_architecture() lr = cfg.TRAIN.LEARNING_RATE lr = args.lr # tr_momentum = cfg.TRAIN.MOMENTUM # tr_momentum = args.momentum params = [] for key, value in dict(FPN.named_parameters()).items(): if value.requires_grad: