| import torch |
| from torch.nn import functional as F |
|
|
| from basicsr.utils.registry import MODEL_REGISTRY |
| from .sr_model import SRModel |
|
|
|
|
| @MODEL_REGISTRY.register() |
| class SwinIRModel(SRModel): |
|
|
| def test(self): |
| |
| 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] |
|
|
