from engineer.models.builder import build_generator, build_backbone from engineer.core.eval import eval_map import os if __name__ == "__main__": args = opt assert args.config is not None, "you must give your model config" cfg = Config.fromfile(args.config) checkpoints = os.path.join(args.load_dirs, "best_checkpoint.pth") assert os.path.exists(checkpoints) # train_data_set = TrainSingerDataset(cfg.data.json_file, transfer=transfer,img_dir = cfg.data.img_dir,black_list=cfg.data.black_list) train_data_set = build_dataset(cfg.data.train) train_loader = DataLoader(train_data_set, batch_size=opt.trainBatch, shuffle=True, num_workers=4, collate_fn=train_loader_collate_fn) test_data_set = build_dataset(cfg.data.test) test_loader = DataLoader(test_data_set, batch_size=opt.validBatch, shuffle=False, num_workers=4, collate_fn=test_loader_collate_fn) if "Alpha.py" in args.config: pose_generator = None
if __name__ == "__main__": args = opt assert args.config is not None, "you must give your model config" cfg = Config.fromfile(args.config) render = build_render(cfg.render_cfg) if cfg.logger: logger = setup_test_logger(cfg.name, rank=args.local_rank) if args.dist: logger.info("Using Distributed test!") # env setup set_up_ddp(cfg, args) info_cfg(logger, cfg) test_data_set = build_dataset(cfg.data.test) checkpoints_path, gallery_id = get_experiments_id(cfg) test_save_path = gallery_id['test'] chamfer_distance = AverageMeter() normal = AverageMeter() p2s = AverageMeter() for i in range(len(test_data_set.subjects)): obj = test_data_set[i] target_mesh_path = obj['mesh_path'] img_mask = np.transpose(obj['mask'][0].numpy(), (1, 2, 0))[..., 0] name = target_mesh_path.split("/")[-1] pred_path = os.path.join(test_save_path, name) target = glob.glob(os.path.join(target_mesh_path, "*.obj"))[-1]