def get_model(args, config): # load model input_channels = int(args.use_multiview) * 128 + int(args.use_normal) * 3 + int(args.use_color) * 3 + int(not args.no_height) model = RefNet( num_class=config.num_class, num_heading_bin=config.num_heading_bin, num_size_cluster=config.num_size_cluster, mean_size_arr=config.mean_size_arr, num_proposal=args.num_proposals, input_feature_dim=input_channels, use_lang_classifier=(not args.no_lang_cls), use_bidir=args.use_bidir, attn=args.self_attn, ).cuda() devices = [int(x) for x in args.devices] print("devices", devices, "torch.cuda.device_count()", torch.cuda.device_count()) model = nn.DataParallel(model, device_ids=devices) model_name = "model_last.pth" if args.detection else "model.pth" path = os.path.join(CONF.PATH.BASE, args.folder, model_name) model.load_state_dict(torch.load(path), strict=False) model.eval() return model
def get_model(args): # load model input_channels = int(args.use_multiview) * 128 + int( args.use_normal) * 3 + int( args.use_color) * 3 + int(not args.no_height) model = RefNet(num_class=DC.num_class, num_heading_bin=DC.num_heading_bin, num_size_cluster=DC.num_size_cluster, mean_size_arr=DC.mean_size_arr, num_proposal=args.num_proposals, input_feature_dim=input_channels).cuda() path = os.path.join(CONF.PATH.OUTPUT, args.folder, "model.pth") model.load_state_dict(torch.load(path), strict=False) model.eval() return model
def get_model(args, config): # load model input_channels = int(args.use_multiview) * 128 + int( args.use_normal) * 3 + int( args.use_color) * 3 + int(not args.no_height) model = RefNet(num_class=config.num_class, num_heading_bin=config.num_heading_bin, num_size_cluster=config.num_size_cluster, mean_size_arr=config.mean_size_arr, num_proposal=args.num_proposals, input_feature_dim=input_channels, use_lang_classifier=(not args.no_lang_cls), use_bidir=args.use_bidir).cuda() model_name = "model_last.pth" if args.detection else "model.pth" path = os.path.join(CONF.PATH.OUTPUT, args.folder, model_name) model.load_state_dict(torch.load(path), strict=False) model.eval() return model