alterzero / DBPN-Pytorch

The project is an official implement of our CVPR2018 paper "Deep Back-Projection Networks for Super-Resolution" (Winner of NTIRE2018 and PIRM2018)
https://alterzero.github.io/projects/DBPN.html
MIT License
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wrong output of pretrained model ‘DBPN-RES-MR64-3’ #44

Open shiqi1994 opened 5 years ago

shiqi1994 commented 5 years ago

Hi! I test my trained model(x4) and your pertained model(x2 x4 x8) which of type 'DBPN-RES-MR64-3' on Set5. But I got results like this: image Here is my test settings: `parser = argparse.ArgumentParser(description='PyTorch Super Res Example') parser.add_argument('--upscale_factor', type=int, default=2, help="super resolution upscale factor") parser.add_argument('--testBatchSize', type=int, default=1, help='testing batch size') parser.add_argument('--gpu_mode', type=bool, default=True) parser.add_argument('--self_ensemble', type=bool, default=False) parser.add_argument('--chop_forward', type=bool, default=False) parser.add_argument('--threads', type=int, default=1, help='number of threads for data loader to use') parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123') parser.add_argument('--gpus', default=1, type=int, help='number of gpu') parser.add_argument('--input_dir', type=str, default='Input') parser.add_argument('--output', default='Results/', help='Location to save checkpoint models') parser.add_argument('--test_dataset', type=str, default='Set5_LR_x2') parser.add_argument('--model_type', type=str, default='DBPN-RES-MR64-3') parser.add_argument('--residual', type=bool, default=False)

parser.add_argument('--model', default='weights/DIV2K_train_HR_size160_step800maiDBPN-RES-MR64-3tpami_residual_filter8_epoch_99.pth', help='sr pretrained base model')

parser.add_argument('--model', default='models/DBPN-RES-MR64-3_2x.pth', help='sr pretrained base model')`

But I test other model like 'DBPNLL' 'DBPN' with different factor, I can get correct result. Could you please help me out? Your early reply will be appreciated. Thank you very much!

shiqi1994 commented 5 years ago

I solved this. Wrong output since I did not set the argument 'residual' from 'false' to 'true'.