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eval_offline.py
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eval_offline.py
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# -*- coding: utf-8 -*-
from calculate_error import *
from tadclip import *
import joblib
import argparse
parser = argparse.ArgumentParser(description='CLIP for Traffic Anomaly Detection',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--height', type=int, default=224) # KITTI (352, 704), DoTA (720, 1280)(480, 880), NYU (416, 544)
parser.add_argument('--width', type=int, default=224)
parser.add_argument('--normal_class', type=int, default=1)
parser.add_argument('--evaluate', type=bool, default=True)
parser.add_argument('--model_dir', type=str, default='./model')
parser.add_argument('--exp_name', type=str, default='TDAFF_BASE_general_fg_hf_add_aafm_catten_cat_f_CLIP_gather_sum')
parser.add_argument('--other_method', type=str, default='TDAFF_BASE')
parser.add_argument('--dataset', type=str, default='dota')
def main():
args = parser.parse_args()
scores = joblib.load(
open(os.path.join(args.model_dir, args.exp_name, "frame_scores_%s_%s_best.json" % (args.height, args.width)), "rb"))
if args.dataset == 'dota':
gt = joblib.load(
open(os.path.join('/data/lrq/DoTA/mnt/workspace/datasets/DoTA_dataset', "ground_truth_demo/gt_label.json"),
"rb"))
elif args.dataset == 'dada':
gt = joblib.load(
open(os.path.join('/data/lrq/DADA-2000', "ground_truth_demo/gt_label.json"),
"rb"))
TAD_result = compute_tad_scores(scores, gt, args, dataset=args.dataset)
print('AUC result: ', TAD_result)
if __name__ == "__main__":
main()