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