inference.py

import argparse
import torch
import os

from models.exaonepath import EXAONEPathV1p5Downstream
from utils.constants import CLASS_NAMES
from tokens import HF_TOKEN


def infer(model, input_file):
    print("Processing", input_file, "...")
    probs = model(input_file)
    result_str = "Result -- " + " / ".join(
        [f"{name}: {probs[i].item():.4f}" for i, name in enumerate(CLASS_NAMES)]
    )
    print(result_str + "\n")


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="Inference")
    parser.add_argument('--svs_dir', type=str, default='./samples', help="")

    args = parser.parse_args()

    hf_token = HF_TOKEN
    model = EXAONEPathV1p5Downstream.from_pretrained("LGAI-EXAONE/EXAONE-Path-1.5", use_auth_token=hf_token)

    model.load_state_dict(torch.load('./pytorch_model.bin'))
    model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
    model.eval()
    model.feature_extractor = torch.compile(model.feature_extractor)
    model.agg_model = torch.compile(model.agg_model)
    
    for svs_name in os.listdir(args.svs_dir):
        infer(model, os.path.join(args.svs_dir, svs_name))