Fix feature decay
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@ -70,6 +70,7 @@ class SRGANModel(BaseModel):
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else:
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raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_fea_type))
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self.l_fea_w = train_opt['feature_weight']
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self.l_fea_w_start = train_opt['feature_weight']
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self.l_fea_w_decay_start = train_opt['feature_weight_decay_start']
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self.l_fea_w_decay_steps = train_opt['feature_weight_decay_steps']
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self.l_fea_w_minimum = train_opt['feature_weight_minimum']
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@ -263,7 +264,7 @@ class SRGANModel(BaseModel):
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# Decay the influence of the feature loss. As the model trains, the GAN will play a stronger role
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# in the resultant image.
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if self.l_fea_w_decay_start and step > self.l_fea_w_decay_start:
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self.l_fea_w = max(self.l_fea_w_minimum, self.l_fea_w - self.l_fea_w_decay_step_size * (step - self.l_fea_w_decay_start))
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self.l_fea_w = max(self.l_fea_w_minimum, self.l_fea_w_start - self.l_fea_w_decay_step_size * (step - self.l_fea_w_decay_start))
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# Note to future self: The BCELoss(0, 1) and BCELoss(0, 0) = .6931
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# Effectively this means that the generator has only completely "won" when l_d_real and l_d_fake is
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