forked from mrq/DL-Art-School
corfea debugging
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@ -176,6 +176,7 @@ class SRGANModel(BaseModel):
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if self.cri_fea: # load VGG perceptual loss
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if self.cri_fea: # load VGG perceptual loss
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self.use_corrupted_feature_input = train_opt['corrupted_feature_input'] if 'corrupted_feature_input' in train_opt.keys() else False
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self.use_corrupted_feature_input = train_opt['corrupted_feature_input'] if 'corrupted_feature_input' in train_opt.keys() else False
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if self.use_corrupted_feature_input:
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if self.use_corrupted_feature_input:
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logger.info("Corrupting inputs into the feature network..")
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self.feature_corruptor = GaussianBlur()
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self.feature_corruptor = GaussianBlur()
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self.netF = networks.define_F(use_bn=False).to(self.device)
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self.netF = networks.define_F(use_bn=False).to(self.device)
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self.lr_netF = None
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self.lr_netF = None
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@ -498,6 +499,8 @@ class SRGANModel(BaseModel):
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elif self.use_corrupted_feature_input:
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elif self.use_corrupted_feature_input:
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cor_Pix = F.interpolate(self.feature_corruptor(pix), size=var_L.shape[2:])
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cor_Pix = F.interpolate(self.feature_corruptor(pix), size=var_L.shape[2:])
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real_fea = self.netF(cor_Pix).detach()
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real_fea = self.netF(cor_Pix).detach()
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if step % 50 == 0:
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utils.save_image(cor_Pix.detach().cpu(), "corrupted_pix.png")
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else:
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else:
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real_fea = self.netF(pix).detach()
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real_fea = self.netF(pix).detach()
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if self.use_corrupted_feature_input:
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if self.use_corrupted_feature_input:
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