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