File size: 2,576 Bytes
f5ba1ae |
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 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
import logging
import torch
from os import path as osp
import sys
try:
sys.path.remove('/xlearning/boyun/codes/MaIR')
except:
print(" ")
sys.path.append('/xlearning/boyun/codes/MaIR/realDenoising')
from basicsr.data import create_dataloader, create_dataset
from basicsr.models import create_model
from basicsr.train import parse_options
from basicsr.utils import (get_env_info, get_root_logger, get_time_str,
make_exp_dirs)
from basicsr.utils.options import dict2str
def main():
# parse options, set distributed setting, set ramdom seed
opt = parse_options(is_train=False)
torch.backends.cudnn.benchmark = True
# torch.backends.cudnn.deterministic = True
# mkdir and initialize loggers
make_exp_dirs(opt)
log_file = osp.join(opt['path']['log'],
f"test_{opt['name']}_{get_time_str()}.log")
logger = get_root_logger(
logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
logger.info(get_env_info())
logger.info(dict2str(opt))
# create test dataset and dataloader
test_loaders = []
for phase, dataset_opt in sorted(opt['datasets'].items()):
test_set = create_dataset(dataset_opt)
test_loader = create_dataloader(
test_set,
dataset_opt,
num_gpu=opt['num_gpu'],
dist=opt['dist'],
sampler=None,
seed=opt['manual_seed'])
logger.info(
f"Number of test images in {dataset_opt['name']}: {len(test_set)}")
test_loaders.append(test_loader)
# create model
# model = create_model(opt)
for i in range(opt['models']['models_num']):
model_idx = opt['models']['models_start'] + opt['models']['models_step'] * i
logger.info(f"Model of {model_idx} steps")
# create model
opt['path']['pretrain_network_g'] = opt['models']['model_root'] + str(model_idx) + '.pth'
model = create_model(opt)
for test_loader in test_loaders:
test_set_name = test_loader.dataset.opt['name']
logger.info(f'Testing {test_set_name}...')
rgb2bgr = opt['val'].get('rgb2bgr', True)
# wheather use uint8 image to compute metrics
use_image = opt['val'].get('use_image', True)
model.validation(
test_loader,
current_iter=opt['name'],
tb_logger=None,
save_img=opt['val']['save_img'],
rgb2bgr=rgb2bgr, use_image=use_image)
if __name__ == '__main__':
main()
|