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import os |
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import time |
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import argparse |
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import cv2 |
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import json |
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import numpy as np |
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from pycocotools import mask as cocomask |
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from metrics import db_eval_iou, db_eval_boundary |
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import multiprocessing as mp |
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import warnings |
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warnings.filterwarnings('ignore') |
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NUM_WOEKERS = 64 |
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def eval_queue(q, rank, out_dict, mevis_pred_path): |
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while not q.empty(): |
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vid_name, exp = q.get() |
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vid = exp_dict[vid_name] |
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exp_name = f'{vid_name}_{exp}' |
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if not os.path.exists(f'{mevis_pred_path}/{vid_name}'): |
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print(f'{vid_name} not found') |
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out_dict[exp_name] = [0, 0] |
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continue |
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pred_0_path = f'{mevis_pred_path}/{vid_name}/{exp}/00000.png' |
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pred_0 = cv2.imread(pred_0_path, cv2.IMREAD_GRAYSCALE) |
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h, w = pred_0.shape |
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vid_len = len(vid['frames']) |
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gt_masks = np.zeros((vid_len, h, w), dtype=np.uint8) |
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pred_masks = np.zeros((vid_len, h, w), dtype=np.uint8) |
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anno_ids = vid['expressions'][exp]['anno_id'] |
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for frame_idx, frame_name in enumerate(vid['frames']): |
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for anno_id in anno_ids: |
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mask_rle = mask_dict[str(anno_id)][frame_idx] |
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if mask_rle: |
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gt_masks[frame_idx] += cocomask.decode(mask_rle) |
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pred_masks[frame_idx] = cv2.imread(f'{mevis_pred_path}/{vid_name}/{exp}/{frame_name}.png', cv2.IMREAD_GRAYSCALE) |
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j = db_eval_iou(gt_masks, pred_masks).mean() |
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f = db_eval_boundary(gt_masks, pred_masks).mean() |
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out_dict[exp_name] = [j, f] |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--mevis_exp_path", type=str, default="datasets/mevis/valid_u/meta_expressions.json") |
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parser.add_argument("--mevis_mask_path", type=str, default="datasets/mevis/valid_u/mask_dict.json") |
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parser.add_argument("--mevis_pred_path", type=str, default="output/mevis/inference") |
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parser.add_argument("--save_name", type=str, default="mevis_test.json") |
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args = parser.parse_args() |
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queue = mp.Queue() |
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exp_dict = json.load(open(args.mevis_exp_path))['videos'] |
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mask_dict = json.load(open(args.mevis_mask_path)) |
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shared_exp_dict = mp.Manager().dict(exp_dict) |
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shared_mask_dict = mp.Manager().dict(mask_dict) |
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output_dict = mp.Manager().dict() |
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for vid_name in exp_dict: |
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vid = exp_dict[vid_name] |
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for exp in vid['expressions']: |
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queue.put([vid_name, exp]) |
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start_time = time.time() |
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processes = [] |
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for rank in range(NUM_WOEKERS): |
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p = mp.Process(target=eval_queue, args=(queue, rank, output_dict, args.mevis_pred_path)) |
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p.start() |
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processes.append(p) |
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for p in processes: |
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p.join() |
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with open(args.save_name, 'w') as f: |
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json.dump(dict(output_dict), f) |
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j = [output_dict[x][0] for x in output_dict] |
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f = [output_dict[x][1] for x in output_dict] |
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print(f'J: {np.mean(j)}') |
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print(f'F: {np.mean(f)}') |
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print(f'J&F: {(np.mean(j) + np.mean(f)) / 2}') |
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end_time = time.time() |
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total_time = end_time - start_time |
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print("time: %.4f s" %(total_time)) |
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