DL-Art-School/codes/scripts/test_psnr_approximator.py
2020-12-18 09:18:34 -07:00

83 lines
3.6 KiB
Python

import os.path as osp
import logging
import time
import argparse
import os
import torchvision
import utils
import utils.options as option
import utils.util as util
from trainer.ExtensibleTrainer import ExtensibleTrainer
from data import create_dataset, create_dataloader
from tqdm import tqdm
import torch
if __name__ == "__main__":
#### options
torch.backends.cudnn.benchmark = True
srg_analyze = False
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../../options/train_psnr_approximator.yml')
opt = option.parse(parser.parse_args().opt, is_train=False)
opt = option.dict_to_nonedict(opt)
utils.util.loaded_options = opt
util.mkdirs(
(path for key, path in opt['path'].items()
if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
screen=True, tofile=True)
logger = logging.getLogger('base')
logger.info(option.dict2str(opt))
#### Create test dataset and dataloader
test_loaders = []
for phase, dataset_opt in sorted(opt['datasets'].items()):
dataset_opt['n_workers'] = 0
test_set = create_dataset(dataset_opt)
test_loader = create_dataloader(test_set, dataset_opt, opt)
logger.info('Number of test images in [{:s}]: {:d}'.format(dataset_opt['name'], len(test_set)))
test_loaders.append(test_loader)
model = ExtensibleTrainer(opt)
for test_loader in test_loaders:
test_set_name = test_loader.dataset.opt['name']
logger.info('\nTesting [{:s}]...'.format(test_set_name))
test_start_time = time.time()
dataset_dir = osp.join(opt['path']['results_root'], test_set_name)
util.mkdir(dataset_dir)
dst_path = "F:\\playground"
[os.makedirs(osp.join(dst_path, str(i)), exist_ok=True) for i in range(10)]
corruptions = ['none', 'color_quantization', 'gaussian_blur', 'motion_blur', 'smooth_blur', 'noise',
'jpeg-medium', 'jpeg-broad', 'jpeg-normal', 'saturation', 'lq_resampling',
'lq_resampling4x']
c_counter = 0
test_set.corruptor.num_corrupts = 0
test_set.corruptor.random_corruptions = []
test_set.corruptor.fixed_corruptions = [corruptions[0]]
corruption_mse = [(0,0) for _ in corruptions]
tq = tqdm(test_loader)
batch_size = opt['datasets']['train']['batch_size']
for data in tq:
need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True
model.feed_data(data, need_GT=need_GT)
model.test()
est_psnr = torch.mean(model.eval_state['psnr_approximate'][0], dim=[1,2,3])
for i in range(est_psnr.shape[0]):
im_path = data['GT_path'][i]
torchvision.utils.save_image(model.eval_state['lq'][0][i], osp.join(dst_path, str(int(est_psnr[i]*10)), osp.basename(im_path)))
#shutil.copy(im_path, osp.join(dst_path, str(int(est_psnr[i]*10))))
last_mse, last_ctr = corruption_mse[c_counter % len(corruptions)]
corruption_mse[c_counter % len(corruptions)] = (last_mse + torch.sum(est_psnr).item(), last_ctr + 1)
c_counter += 1
test_set.corruptor.fixed_corruptions = [corruptions[c_counter % len(corruptions)]]
if c_counter % 100 == 0:
for i, (mse, ctr) in enumerate(corruption_mse):
print("%s: %f" % (corruptions[i], mse / (ctr * batch_size)))