| import torch |
| import tqdm |
|
|
| from evaluation.dataset import HDMapNetEvalDataset |
| from evaluation.chamfer_distance import semantic_mask_chamfer_dist_cum |
| from evaluation.AP import instance_mask_AP |
| from evaluation.iou import get_batch_iou |
|
|
| SAMPLED_RECALLS = torch.linspace(0.1, 1, 10) |
| THRESHOLDS = [2, 4, 6] |
|
|
|
|
| def get_val_info(args): |
| data_conf = { |
| 'xbound': args.xbound, |
| 'ybound': args.ybound, |
| 'thickness': args.thickness, |
| } |
|
|
| dataset = HDMapNetEvalDataset(args.version, args.dataroot, args.eval_set, args.result_path, data_conf) |
|
|
| data_loader = torch.utils.data.DataLoader(dataset, batch_size=args.bsz, shuffle=False, drop_last=False) |
|
|
| total_CD1 = torch.zeros(args.max_channel).cuda() |
| total_CD2 = torch.zeros(args.max_channel).cuda() |
| total_CD_num1 = torch.zeros(args.max_channel).cuda() |
| total_CD_num2 = torch.zeros(args.max_channel).cuda() |
| total_intersect = torch.zeros(args.max_channel).cuda() |
| total_union = torch.zeros(args.max_channel).cuda() |
| AP_matrix = torch.zeros((args.max_channel, len(THRESHOLDS))).cuda() |
| AP_count_matrix = torch.zeros((args.max_channel, len(THRESHOLDS))).cuda() |
|
|
| print('running eval...') |
| for pred_map, confidence_level, gt_map in tqdm.tqdm(data_loader): |
| |
| pred_map = pred_map.cuda() |
| confidence_level = confidence_level.cuda() |
| gt_map = gt_map.cuda() |
|
|
| intersect, union = get_batch_iou(pred_map, gt_map) |
| CD1, CD2, num1, num2 = semantic_mask_chamfer_dist_cum(pred_map, gt_map, args.xbound[2], args.ybound[2], threshold=args.CD_threshold) |
|
|
| instance_mask_AP(AP_matrix, AP_count_matrix, pred_map, gt_map, args.xbound[2], args.ybound[2], |
| confidence_level, THRESHOLDS, sampled_recalls=SAMPLED_RECALLS) |
|
|
| total_intersect += intersect.cuda() |
| total_union += union.cuda() |
| total_CD1 += CD1 |
| total_CD2 += CD2 |
| total_CD_num1 += num1 |
| total_CD_num2 += num2 |
|
|
| CD_pred = total_CD1 / total_CD_num1 |
| CD_label = total_CD2 / total_CD_num2 |
| CD = (total_CD1 + total_CD2) / (total_CD_num1 + total_CD_num2) |
| CD_pred[CD_pred > args.CD_threshold] = args.CD_threshold |
| CD_label[CD_label > args.CD_threshold] = args.CD_threshold |
| CD[CD > args.CD_threshold] = args.CD_threshold |
| return { |
| 'iou': total_intersect / total_union, |
| 'CD_pred': CD_pred, |
| 'CD_label': CD_label, |
| 'CD': CD, |
| 'Average_precision': AP_matrix / AP_count_matrix, |
| } |
|
|
|
|
| if __name__ == '__main__': |
| import argparse |
| parser = argparse.ArgumentParser(description='Evaluate nuScenes local HD Map Construction Results.') |
| parser.add_argument('--result_path', type=str) |
| parser.add_argument('--dataroot', type=str, default='dataset/nuScenes/') |
| parser.add_argument('--bsz', type=int, default=4) |
| parser.add_argument('--version', type=str, default='v1.0-mini', choices=['v1.0-trainval', 'v1.0-mini']) |
| parser.add_argument('--eval_set', type=str, default='mini_val', choices=['train', 'val', 'test', 'mini_train', 'mini_val']) |
| parser.add_argument('--thickness', type=int, default=5) |
| parser.add_argument('--max_channel', type=int, default=3) |
| parser.add_argument('--CD_threshold', type=int, default=5) |
| parser.add_argument("--xbound", nargs=3, type=float, default=[-30.0, 30.0, 0.15]) |
| parser.add_argument("--ybound", nargs=3, type=float, default=[-15.0, 15.0, 0.15]) |
|
|
| args = parser.parse_args() |
|
|
| print(get_val_info(args)) |
|
|
