Only log discriminator data when gan is activated
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@ -347,13 +347,15 @@ class SRGANModel(BaseModel):
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if self.cri_fea:
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self.add_log_entry('feature_weight', self.l_fea_w)
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self.add_log_entry('l_g_fea', l_g_fea.item())
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self.add_log_entry('l_g_gan', l_g_gan.item())
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if self.l_gan_w > 0:
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self.add_log_entry('l_g_gan', l_g_gan.item())
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self.add_log_entry('l_g_total', l_g_total.item() * self.mega_batch_factor)
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self.add_log_entry('l_d_real', l_d_real.item() * self.mega_batch_factor)
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self.add_log_entry('l_d_fake', l_d_fake.item() * self.mega_batch_factor)
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self.add_log_entry('D_fake', torch.mean(pred_d_fake.detach()))
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self.add_log_entry('D_diff', torch.mean(pred_d_fake) - torch.mean(pred_d_real))
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self.add_log_entry('noise_theta', noise_theta)
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if self.l_gan_w > 0:
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self.add_log_entry('l_d_real', l_d_real.item() * self.mega_batch_factor)
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self.add_log_entry('l_d_fake', l_d_fake.item() * self.mega_batch_factor)
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self.add_log_entry('D_fake', torch.mean(pred_d_fake.detach()))
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self.add_log_entry('D_diff', torch.mean(pred_d_fake) - torch.mean(pred_d_real))
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self.add_log_entry('noise_theta', noise_theta)
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if step % self.corruptor_swapout_steps == 0 and step > 0:
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self.load_random_corruptor()
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