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]