DL-Art-School/codes/test.py

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import os.path as osp
import logging
import time
import argparse
from collections import OrderedDict
import options.options as option
import utils.util as util
from data.util import bgr2ycbcr
from data import create_dataset, create_dataloader
from models import create_model
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from tqdm import tqdm
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if __name__ == "__main__":
#### options
want_just_images = True
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to options YMAL file.', default='options/test/test_corrupt_vixen_adrianna.yml')
opt = option.parse(parser.parse_args().opt, is_train=False)
opt = option.dict_to_nonedict(opt)
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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))
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#### 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)
logger.info('Number of test images in [{:s}]: {:d}'.format(dataset_opt['name'], len(test_set)))
test_loaders.append(test_loader)
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model = create_model(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)
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test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
test_results['psnr_y'] = []
test_results['ssim_y'] = []
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tq = tqdm(test_loader)
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()
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visuals = model.fake_H.detach().float().cpu()
for i in range(visuals.shape[0]):
img_path = data['GT_path'][i] if need_GT else data['LQ_path'][i]
img_name = osp.splitext(osp.basename(img_path))[0]
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sr_img = util.tensor2img(visuals[i]) # uint8
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# save images
suffix = opt['suffix']
if suffix:
save_img_path = osp.join(dataset_dir, img_name + suffix + '.png')
else:
save_img_path = osp.join(dataset_dir, img_name + '.png')
util.save_img(sr_img, save_img_path)
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if want_just_images:
continue
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# calculate PSNR and SSIM
if need_GT:
gt_img = util.tensor2img(visuals['GT'])
sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale'])
psnr = util.calculate_psnr(sr_img, gt_img)
ssim = util.calculate_ssim(sr_img, gt_img)
test_results['psnr'].append(psnr)
test_results['ssim'].append(ssim)
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if gt_img.shape[2] == 3: # RGB image
sr_img_y = bgr2ycbcr(sr_img / 255., only_y=True)
gt_img_y = bgr2ycbcr(gt_img / 255., only_y=True)
psnr_y = util.calculate_psnr(sr_img_y * 255, gt_img_y * 255)
ssim_y = util.calculate_ssim(sr_img_y * 255, gt_img_y * 255)
test_results['psnr_y'].append(psnr_y)
test_results['ssim_y'].append(ssim_y)
logger.info(
'{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}.'.
format(img_name, psnr, ssim, psnr_y, ssim_y))
else:
logger.info('{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}.'.format(img_name, psnr, ssim))
else:
logger.info(img_name)
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if not want_just_images and need_GT: # metrics
# Average PSNR/SSIM results
ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
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logger.info(
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'----Average PSNR/SSIM results for {}----\n\tPSNR: {:.6f} dB; SSIM: {:.6f}\n'.format(
test_set_name, ave_psnr, ave_ssim))
if test_results['psnr_y'] and test_results['ssim_y']:
ave_psnr_y = sum(test_results['psnr_y']) / len(test_results['psnr_y'])
ave_ssim_y = sum(test_results['ssim_y']) / len(test_results['ssim_y'])
logger.info(
'----Y channel, average PSNR/SSIM----\n\tPSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}\n'.
format(ave_psnr_y, ave_ssim_y))