-
Notifications
You must be signed in to change notification settings - Fork 13
/
evaluate.py
38 lines (28 loc) · 1.18 KB
/
evaluate.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
import logging
import os
#import cv2
import sys
import torch
#import backbones.mixnetm as mx
from backbones import iresnet100
from utils.utils_callbacks import CallBackVerification
from utils.utils_logging import init_logging
sys.path.append('/root/xy/work_dir/xyface/')
from config import config as cfg
if __name__ == "__main__":
load_color = True
both_masked = False
gpu_id = 3
log_root = logging.getLogger()
init_logging(log_root, 0, cfg.output,logfile="test_student2_single.log")
callback_verification = CallBackVerification(1, 0, cfg.val_targets, cfg.rec, load_color=load_color, both_masked=both_masked)
output_folder='/home/fboutros/Masked-Face-Recognition-KD/eval'
weights=os.listdir(output_folder)
with torch.no_grad():
for w in weights:
if "backbone" in w:
print("Evaluating:", w)
backbone = iresnet100(num_features=cfg.embedding_size).to(f"cuda:{gpu_id}")
backbone.load_state_dict(torch.load(os.path.join(output_folder,w)))
model = torch.nn.DataParallel(backbone, device_ids=[gpu_id])
callback_verification(int(w.split("backbone")[0]),backbone)