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###########################################################################
# Created by: BUAA
# Email: clyanhh@gmail.com
# Copyright (c) 2024
###########################################################################
import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning)
import os.path as osp
import time
import argparse
import json
import numpy as np
import multiprocessing as mp
import pandas as pd
from pycocotools import mask as cocomask
from third_parts.revos.utils.metircs import db_eval_iou, db_eval_boundary, get_r2vos_accuracy, get_r2vos_robustness
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]
vid_len, h, w = len(vid['frames']), vid['height'], vid['width']
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']):
# all instances in the same frame
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()
a = get_r2vos_accuracy(gt_masks, pred_masks).mean()
out_dict[exp_name] = [j, f, a]
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("pred_path", type=str, )
parser.add_argument("--exp_path", type=str, default="data/video_datas/revos/meta_expressions_valid_.json")
parser.add_argument("--mask_path", type=str, default="data/video_datas/revos/mask_dict.json")
parser.add_argument("--save_json_name", type=str, default="revos_valid.json")
parser.add_argument("--save_csv_name", type=str, default="revos_valid.csv")
args = parser.parse_args()
queue = mp.Queue()
exp_dict = json.load(open(args.exp_path))['videos']
mask_dict = json.load(open(args.mask_path))
mask_pred = json.load(open(args.pred_path))
shared_exp_dict = mp.Manager().dict(exp_dict)
shared_mask_dict = mp.Manager().dict(mask_dict)
output_dict = mp.Manager().dict()
mask_pred_dict = mp.Manager().dict(mask_pred)
for idx, vid_name in enumerate(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)
# save average results
output_json_path = osp.join(osp.dirname(args.pred_path), args.save_json_name)
output_csv_path = osp.join(osp.dirname(args.pred_path), args.save_csv_name)
data_list = []
for videxp, (j, f, a) in output_dict.items():
vid_name, exp = videxp.rsplit('_', 1)
data = {}
data['video_name'] = vid_name
data['exp_id'] = exp
data['exp'] = exp_dict[vid_name]['expressions'][exp]['exp']
data['videxp'] = videxp
data['J'] = round(100 * j, 2)
data['F'] = round(100 * f, 2)
data['JF'] = round(100 * (j + f) / 2, 2)
data['A'] = round(100 * a, 2)
data['type_id'] = exp_dict[vid_name]['expressions'][exp]['type_id']
data_list.append(data)
is_long = lambda x: x['type_id'] == 0
is_short = lambda x: x['type_id'] == 1
j_referring = np.array([d['J'] for d in data_list if is_long(d)]).mean()
f_referring = np.array([d['F'] for d in data_list if is_long(d)]).mean()
a_referring = np.array([d['A'] for d in data_list if is_long(d)]).mean()
jf_referring = (j_referring + f_referring) / 2
j_reason = np.array([d['J'] for d in data_list if is_short(d)]).mean()
f_reason = np.array([d['F'] for d in data_list if is_short(d)]).mean()
a_reason = np.array([d['A'] for d in data_list if is_short(d)]).mean()
jf_reason = (j_reason + f_reason) / 2
j_referring_reason = (j_referring + j_reason) / 2
f_referring_reason = (f_referring + f_reason) / 2
a_referring_reason = (a_referring + a_reason) / 2
jf_referring_reason = (jf_referring + jf_reason) / 2
results = {
"long": {
"J" : j_referring,
"F" : f_referring,
"A" : a_referring,
"JF": jf_referring
},
"short": {
"J" : j_reason,
"F" : f_reason,
"A" : a_reason,
"JF": jf_reason
},
"overall": {
"J" : j_referring_reason,
"F" : f_referring_reason,
"A" : a_referring_reason,
"JF": jf_referring_reason
}
}
print(results)
with open(output_json_path, 'w') as f:
json.dump(results, f, indent=4)
print(f"Results saved to {output_json_path}")
data4csv = {}
for data in data_list:
for k, v in data.items():
data4csv[k] = data4csv.get(k, []) + [v]
df = pd.DataFrame(data4csv)
df.to_csv(output_csv_path, index=False)
print(f"Results saved to {output_csv_path}")
end_time = time.time()
total_time = end_time - start_time
print("time: %.4f s" %(total_time))
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