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test.py
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import os.path as osp
import logging
import time
import argparse
from collections import OrderedDict
import numpy as np
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
#### options
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, required=True, help='Path to options YMAL file.')
opt = option.parse(parser.parse_args().opt, is_train=False)
opt = option.dict_to_nonedict(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()):
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)
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)
test_results = OrderedDict()
#HR images
test_results['psnr'] = []
test_results['ssim'] = []
test_results['psnr_y'] = []
test_results['ssim_y'] = []
# LR images
test_results['psnr_lr'] = []
test_results['ssim_lr'] = []
test_results['psnr_y_lr'] = []
test_results['ssim_y_lr'] = []
for data in test_loader:
model.feed_data(data)
img_path = data['GT_path'][0]
img_name = osp.splitext(osp.basename(img_path))[0]
model.test()
visuals = model.get_current_visuals()
sr_img = util.tensor2img(visuals['SR']) # uint8
srgt_img = util.tensor2img(visuals['GT']) # uint8
lr_img = util.tensor2img(visuals['LR']) # uint8
lrgt_img = util.tensor2img(visuals['LR_ref']) # uint8
# save images suffix
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)
if suffix:
save_img_path = osp.join(dataset_dir, img_name + suffix + '_GT.png')
else:
save_img_path = osp.join(dataset_dir, img_name + '_GT.png')
util.save_img(srgt_img, save_img_path)
if suffix:
save_img_path = osp.join(dataset_dir, img_name + suffix + '_LR.png')
else:
save_img_path = osp.join(dataset_dir, img_name + '_LR.png')
util.save_img(lr_img, save_img_path)
if suffix:
save_img_path = osp.join(dataset_dir, img_name + suffix + '_LR_ref.png')
else:
save_img_path = osp.join(dataset_dir, img_name + '_LR_ref.png')
util.save_img(lrgt_img, save_img_path)
# calculate PSNR and SSIM
gt_img = util.tensor2img(visuals['GT'])
gt_img = gt_img / 255.
sr_img = sr_img / 255.
lr_img = lr_img / 255.
lrgt_img = lrgt_img / 255.
crop_border = opt['crop_border'] if opt['crop_border'] else opt['scale']
if crop_border == 0:
cropped_sr_img = sr_img
cropped_gt_img = gt_img
else:
cropped_sr_img = sr_img[crop_border:-crop_border, crop_border:-crop_border, :]
cropped_gt_img = gt_img[crop_border:-crop_border, crop_border:-crop_border, :]
#PSNR and SSIM for HR
psnr = util.calculate_psnr(cropped_sr_img * 255, cropped_gt_img * 255)
ssim = util.calculate_ssim(cropped_sr_img * 255, cropped_gt_img * 255)
test_results['psnr'].append(psnr)
test_results['ssim'].append(ssim)
# PSNR and SSIM for LR
psnr_lr = util.calculate_psnr(lr_img * 255, lrgt_img * 255)
ssim_lr = util.calculate_ssim(lr_img * 255, lrgt_img * 255)
test_results['psnr_lr'].append(psnr_lr)
test_results['ssim_lr'].append(ssim_lr)
if gt_img.shape[2] == 3: # RGB image
sr_img_y = bgr2ycbcr(sr_img, only_y=True)
gt_img_y = bgr2ycbcr(gt_img, only_y=True)
if crop_border == 0:
cropped_sr_img_y = sr_img_y
cropped_gt_img_y = gt_img_y
else:
cropped_sr_img_y = sr_img_y[crop_border:-crop_border, crop_border:-crop_border]
cropped_gt_img_y = gt_img_y[crop_border:-crop_border, crop_border:-crop_border]
psnr_y = util.calculate_psnr(cropped_sr_img_y * 255, cropped_gt_img_y * 255)
ssim_y = util.calculate_ssim(cropped_sr_img_y * 255, cropped_gt_img_y * 255)
test_results['psnr_y'].append(psnr_y)
test_results['ssim_y'].append(ssim_y)
lr_img_y = bgr2ycbcr(lr_img, only_y=True)
lrgt_img_y = bgr2ycbcr(lrgt_img, only_y=True)
psnr_y_lr = util.calculate_psnr(lr_img_y * 255, lrgt_img_y * 255)
ssim_y_lr = util.calculate_ssim(lr_img_y * 255, lrgt_img_y * 255)
test_results['psnr_y_lr'].append(psnr_y_lr)
test_results['ssim_y_lr'].append(ssim_y_lr)
logger.info(
'{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}. LR PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}.'.
format(img_name, psnr, ssim, psnr_y, ssim_y, psnr_lr, ssim_lr, psnr_y_lr, ssim_y_lr))
else:
logger.info('{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}. LR PSNR: {:.6f} dB; SSIM: {:.6f}.'.format(img_name, psnr, ssim, psnr_lr, ssim_lr))
# Average PSNR/SSIM results
ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
ave_psnr_lr = sum(test_results['psnr_lr']) / len(test_results['psnr_lr'])
ave_ssim_lr = sum(test_results['ssim_lr']) / len(test_results['ssim_lr'])
logger.info(
'----Average PSNR/SSIM results for {}----\n\tpsnr: {:.6f} db; ssim: {:.6f}. LR psnr: {:.6f} db; ssim: {:.6f}.\n'.format(
test_set_name, ave_psnr, ave_ssim, ave_psnr_lr, ave_ssim_lr))
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'])
ave_psnr_y_lr = sum(test_results['psnr_y_lr']) / len(test_results['psnr_y_lr'])
ave_ssim_y_lr = sum(test_results['ssim_y_lr']) / len(test_results['ssim_y_lr'])
logger.info(
'----Y channel, average PSNR/SSIM----\n\tPSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}. LR PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}.\n'.
format(ave_psnr_y, ave_ssim_y, ave_psnr_y_lr, ave_ssim_y_lr))