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_path', type=str, default='./samples/wsis/1/1.svs', help="Path to the .svs file")
    parser.add_argument('--svs_dir', type=str, default='./samples_CRC', 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 = EXAONEPathV1p5Downstream(num_sampled_patch=16384)


    # qwe = torch.load('./pytorch_model_ori.bin')
    # aaa = model.load_state_dict(qwe, strict=False)
    # hw_w = torch.load('/mnt/shared/shared_medical/shared/hi.choi/MOM/logs_eval_25/closebench2/ours/BIOMARKER_SMC_SMC/CRCSensor/CRCSensor_exaone_mom3_MOM_batch_8_lr0.00003_wd0.1_do0.1/_s100/s_0_checkpoint.pt', map_location='cpu')

    # new_state_dict = {}
    # for k, v in hw_w.items():
    #     if k.startswith("_orig_mod."):
    #         new_k = k.replace("_orig_mod.", "agg_model.", 1)
    #     else:
    #         new_k = k
    #     new_state_dict[new_k] = v

    # load_result = model.load_state_dict(new_state_dict, strict=False)
    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))