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
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import logging
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import time
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import argparse
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from collections import OrderedDict
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import options.options as option
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import utils.util as util
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from data.util import bgr2ycbcr
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from data import create_dataset, create_dataloader
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from models import create_model
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from tqdm import tqdm
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if __name__ == "__main__":
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#### options
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want_just_images = True
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YMAL file.', default='options/test/test_ESRGAN_adrianna_full.yml')
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opt = option.parse(parser.parse_args().opt, is_train=False)
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opt = option.dict_to_nonedict(opt)
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util.mkdirs(
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(path for key, path in opt['path'].items()
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if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
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util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
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screen=True, tofile=True)
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logger = logging.getLogger('base')
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logger.info(option.dict2str(opt))
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#### Create test dataset and dataloader
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test_loaders = []
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for phase, dataset_opt in sorted(opt['datasets'].items()):
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test_set = create_dataset(dataset_opt)
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test_loader = create_dataloader(test_set, dataset_opt)
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logger.info('Number of test images in [{:s}]: {:d}'.format(dataset_opt['name'], len(test_set)))
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test_loaders.append(test_loader)
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model = create_model(opt)
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for test_loader in test_loaders:
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test_set_name = test_loader.dataset.opt['name']
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logger.info('\nTesting [{:s}]...'.format(test_set_name))
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test_start_time = time.time()
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dataset_dir = osp.join(opt['path']['results_root'], test_set_name)
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util.mkdir(dataset_dir)
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test_results = OrderedDict()
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test_results['psnr'] = []
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test_results['ssim'] = []
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test_results['psnr_y'] = []
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test_results['ssim_y'] = []
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tq = tqdm(test_loader)
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for data in tq:
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need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True
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model.feed_data(data, need_GT=need_GT)
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model.test()
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visuals = model.fake_H.detach().float().cpu()
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for i in range(visuals.shape[0]):
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img_path = data['GT_path'][i] if need_GT else data['LQ_path'][i]
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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
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suffix = opt['suffix']
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if suffix:
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save_img_path = osp.join(dataset_dir, img_name + suffix + '.png')
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else:
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save_img_path = osp.join(dataset_dir, img_name + '.png')
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util.save_img(sr_img, save_img_path)
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if want_just_images:
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continue
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# calculate PSNR and SSIM
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if need_GT:
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gt_img = util.tensor2img(visuals['GT'])
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sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale'])
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psnr = util.calculate_psnr(sr_img, gt_img)
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ssim = util.calculate_ssim(sr_img, gt_img)
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test_results['psnr'].append(psnr)
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test_results['ssim'].append(ssim)
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if gt_img.shape[2] == 3: # RGB image
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sr_img_y = bgr2ycbcr(sr_img / 255., only_y=True)
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gt_img_y = bgr2ycbcr(gt_img / 255., only_y=True)
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psnr_y = util.calculate_psnr(sr_img_y * 255, gt_img_y * 255)
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ssim_y = util.calculate_ssim(sr_img_y * 255, gt_img_y * 255)
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test_results['psnr_y'].append(psnr_y)
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test_results['ssim_y'].append(ssim_y)
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logger.info(
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'{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}.'.
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format(img_name, psnr, ssim, psnr_y, ssim_y))
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else:
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logger.info('{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}.'.format(img_name, psnr, ssim))
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else:
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logger.info(img_name)
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if not want_just_images and need_GT: # metrics
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# Average PSNR/SSIM results
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ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
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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(
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test_set_name, ave_psnr, ave_ssim))
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if test_results['psnr_y'] and test_results['ssim_y']:
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ave_psnr_y = sum(test_results['psnr_y']) / len(test_results['psnr_y'])
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ave_ssim_y = sum(test_results['ssim_y']) / len(test_results['ssim_y'])
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logger.info(
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'----Y channel, average PSNR/SSIM----\n\tPSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}\n'.
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format(ave_psnr_y, ave_ssim_y))
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