def evaluate(model_weight=None):
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument("--batch_size", type=int, default=128)
arg_parser.add_argument("--num_instances", type=int, default=1)
arg_parser.add_argument("--model_weight",
type=str,
default=f"results/train/weights/{model_weight}.pt")
args = arg_parser.parse_args()
train_meta = pd.read_csv(
"/datasets/objstrgzip/03_face_verification_angle/train/train_meta.csv")
num_classes = len(set(train_meta["face_id"].values))
# 모델 및 weight 로드
model = Triarchy(args, num_classes, train=False)
model.load_state_dict(torch.load(args.model_weight))
print(f"Loaded weight {args.model_weight}")
print(f"Number of Parameters: {count_parameters(model)}")
# Trainer 인스턴스 생성 및 data loader 로드
trainer = Trainer(model, args, logging=False)
data_loader = trainer.model.get_test_data_loader("test", "test_label.csv")
# Evaluation
trainer.eval(data_loader, train=False, save=True)
