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| |
| |
| |
| import warnings |
|
|
| warnings.filterwarnings("ignore", category=RuntimeWarning) |
| import os.path as osp |
| import time |
| import argparse |
| import json |
| import numpy as np |
| from pycocotools import mask as cocomask |
| from third_parts.revos.utils.metircs import db_eval_iou, db_eval_boundary |
| import multiprocessing as mp |
|
|
| NUM_WOEKERS = 128 |
|
|
|
|
| def eval_queue(q, rank, out_dict): |
| while not q.empty(): |
| |
| vid_name, exp = q.get() |
|
|
| vid = exp_dict[vid_name] |
|
|
| exp_name = f'{vid_name}_{exp}' |
|
|
| pred = mask_pred_dict[vid_name][exp] |
|
|
| h, w = pred['prediction_masks'][0]['size'] |
| vid_len = len(vid['frames']) |
| gt_masks = np.zeros((vid_len, h, w), dtype=np.uint8) |
| pred_masks = np.zeros((vid_len, h, w), dtype=np.uint8) |
|
|
| anno_ids = vid['expressions'][exp]['anno_id'] |
|
|
| for frame_idx, frame_name in enumerate(vid['frames']): |
| for anno_id in anno_ids: |
| mask_rle = mask_dict[str(anno_id)][frame_idx] |
| if mask_rle: |
| gt_masks[frame_idx] += cocomask.decode(mask_rle) |
|
|
| pred_mask = cocomask.decode(pred['prediction_masks'][frame_idx]) |
| pred_masks[frame_idx] += pred_mask |
|
|
| j = db_eval_iou(gt_masks, pred_masks).mean() |
| f = db_eval_boundary(gt_masks, pred_masks).mean() |
| out_dict[exp_name] = [j, f] |
|
|
|
|
| if __name__ == '__main__': |
| parser = argparse.ArgumentParser() |
| parser.add_argument("pred_path", type=str, ) |
| parser.add_argument("--mevis_exp_path", type=str, |
| default="./data/video_datas/mevis/valid_u/meta_expressions.json") |
| parser.add_argument("--mevis_mask_path", type=str, |
| default="./data/video_datas/mevis/valid_u/mask_dict.json") |
| parser.add_argument("--save_name", type=str, default="mevis_valu.json") |
| args = parser.parse_args() |
| queue = mp.Queue() |
| exp_dict = json.load(open(args.mevis_exp_path))['videos'] |
| mask_dict = json.load(open(args.mevis_mask_path)) |
|
|
| shared_exp_dict = mp.Manager().dict(exp_dict) |
| shared_mask_dict = mp.Manager().dict(mask_dict) |
| output_dict = mp.Manager().dict() |
|
|
| mask_pred = json.load(open(args.pred_path)) |
| mask_pred_dict = mp.Manager().dict(mask_pred) |
|
|
| for vid_name in exp_dict: |
| vid = exp_dict[vid_name] |
| for exp in vid['expressions']: |
| queue.put([vid_name, exp]) |
|
|
| start_time = time.time() |
| if NUM_WOEKERS > 1: |
| processes = [] |
| for rank in range(NUM_WOEKERS): |
| p = mp.Process(target=eval_queue, args=(queue, rank, output_dict)) |
| p.start() |
| processes.append(p) |
|
|
| for p in processes: |
| p.join() |
| else: |
| eval_queue(queue, 0, output_dict) |
|
|
| j = [output_dict[x][0] for x in output_dict] |
| f = [output_dict[x][1] for x in output_dict] |
|
|
| output_path = osp.join(osp.dirname(args.pred_path), '..', args.save_name) |
| results = { |
| 'J': round(100 * float(np.mean(j)), 2), |
| 'F': round(100 * float(np.mean(f)), 2), |
| 'J&F': round(100 * float((np.mean(j) + np.mean(f)) / 2), 2), |
| } |
| with open(output_path, 'w') as f: |
| json.dump(results, f, indent=4) |
|
|
| print(json.dumps(results, indent=4)) |
|
|
| end_time = time.time() |
| total_time = end_time - start_time |
| print("time: %.4f s" % (total_time)) |
|
|
|
|
