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| import torch | |
| from torch.nn import functional as F | |
| from basicsr.utils.registry import MODEL_REGISTRY | |
| from .sr_model import SRModel | |
| class SwinIRModel(SRModel): | |
| def test(self): | |
| # pad to multiplication of window_size | |
| window_size = self.opt['network_g']['window_size'] | |
| scale = self.opt.get('scale', 1) | |
| mod_pad_h, mod_pad_w = 0, 0 | |
| _, _, h, w = self.lq.size() | |
| if h % window_size != 0: | |
| mod_pad_h = window_size - h % window_size | |
| if w % window_size != 0: | |
| mod_pad_w = window_size - w % window_size | |
| img = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect') | |
| if hasattr(self, 'net_g_ema'): | |
| self.net_g_ema.eval() | |
| with torch.no_grad(): | |
| self.output = self.net_g_ema(img) | |
| else: | |
| self.net_g.eval() | |
| with torch.no_grad(): | |
| self.output = self.net_g(img) | |
| self.net_g.train() | |
| _, _, h, w = self.output.size() | |
| self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale] | |