forked from mrq/DL-Art-School
44b89330c2
This is a checkpoint of a set of long tests with reduced-complexity networks. Some takeaways:
1) A full GAN using the resnet discriminator does appear to converge, but the quality is capped.
2) Likewise, a combination GAN/feature loss does not converge. The feature loss is optimized but
the model appears unable to fight the discriminator, so the G-loss steadily increases.
Going forwards, I want to try some bigger models. In particular, I want to change the generator
to increase complexity and capacity. I also want to add skip connections between the
disc and generator.
114 lines
5.0 KiB
Python
114 lines
5.0 KiB
Python
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
|
|
from tqdm import tqdm
|
|
|
|
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_ESRGAN_adrianna_full.yml')
|
|
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()
|
|
test_results['psnr'] = []
|
|
test_results['ssim'] = []
|
|
test_results['psnr_y'] = []
|
|
test_results['ssim_y'] = []
|
|
|
|
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()
|
|
|
|
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]
|
|
|
|
sr_img = util.tensor2img(visuals[i]) # uint8
|
|
|
|
# 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)
|
|
|
|
if want_just_images:
|
|
continue
|
|
|
|
# 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)
|
|
|
|
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)
|
|
|
|
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'])
|
|
logger.info(
|
|
'----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))
|