forked from mrq/DL-Art-School
109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
import os.path as osp
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import logging
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import random
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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 utils
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import utils.options as option
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import utils.util as util
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from trainer.ExtensibleTrainer import ExtensibleTrainer
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from data import create_dataset, create_dataloader
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from tqdm import tqdm
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import torch
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import numpy as np
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def forward_pass(model, data, output_dir, opt):
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alteration_suffix = util.opt_get(opt, ['name'], '')
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denorm_range = tuple(util.opt_get(opt, ['image_normalization_range'], [0, 1]))
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with torch.no_grad():
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model.feed_data(data, 0, need_GT=need_GT)
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model.test()
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visuals = model.get_current_visuals(need_GT)['rlt'].cpu()
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visuals = (visuals - denorm_range[0]) / (denorm_range[1]-denorm_range[0])
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fea_loss = 0
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psnr_loss = 0
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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 = alteration_suffix
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if suffix:
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save_img_path = osp.join(output_dir, img_name + suffix + '.png')
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else:
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save_img_path = osp.join(output_dir, img_name + '.png')
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if need_GT:
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psnr_sr = util.tensor2img(visuals[i])
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psnr_gt = util.tensor2img(data['hq'][i])
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psnr_loss += util.calculate_psnr(psnr_sr, psnr_gt)
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util.save_img(sr_img, save_img_path)
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return fea_loss, psnr_loss
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if __name__ == "__main__":
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# Set seeds
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torch.manual_seed(5555)
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random.seed(5555)
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np.random.seed(5555)
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#### options
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torch.backends.cudnn.benchmark = True
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want_metrics = False
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_diffusion_unet_sm.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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utils.util.loaded_options = 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 = ExtensibleTrainer(opt)
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fea_loss = 0
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psnr_loss = 0
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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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need_GT = need_GT and want_metrics
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fea_loss, psnr_loss = forward_pass(model, data, dataset_dir, opt)
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fea_loss += fea_loss
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psnr_loss += psnr_loss
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# log
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logger.info('# Validation # Fea: {:.4e}, PSNR: {:.4e}'.format(fea_loss / len(test_loader), psnr_loss / len(test_loader)))
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