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import logging |
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import torch |
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from os import path as osp |
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from basicsr.data import build_dataloader, build_dataset |
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from basicsr.models import build_model |
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from basicsr.utils import get_root_logger, get_time_str, make_exp_dirs |
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from basicsr.utils.options import dict2str, parse_options |
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def image_sr(args): |
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opt, _ = parse_options(args.root_path, is_train=False) |
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torch.backends.cudnn.benchmark = True |
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test_loaders = [] |
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for _, dataset_opt in sorted(opt['datasets'].items()): |
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dataset_opt['dataroot_lq'] = osp.join(args.output_dir, f'temp_LR') |
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if args.SR == 'x4': |
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opt['upscale'] = opt['network_g']['upscale'] = 4 |
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opt['val']['suffix'] = 'x4' |
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opt['path']['pretrain_network_g'] = osp.join(args.root_path, f'experiments/pretrained_models/RGT_x4.pth') |
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if args.SR == 'x2': |
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opt['upscale'] = opt['network_g']['upscale'] = 2 |
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opt['val']['suffix'] = 'x2' |
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test_set = build_dataset(dataset_opt) |
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test_loader = build_dataloader( |
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test_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed']) |
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test_loaders.append(test_loader) |
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opt['path']['pretrain_network_g'] = args.ckpt_path |
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opt['val']['use_chop'] = args.use_chop |
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opt['path']['visualization'] = osp.join(args.output_dir, f'temp_results') |
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opt['path']['results_root'] = osp.join(args.output_dir, f'temp_results') |
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model = build_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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model.validation(test_loader, current_iter=opt['name'], tb_logger=None, save_img=opt['val']['save_img']) |
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if __name__ == '__main__': |
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root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) |
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