forked from mrq/DL-Art-School
Misc fixes
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@ -102,7 +102,7 @@ class SRGANModel(BaseModel):
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# D_update_ratio and D_init_iters
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self.D_update_ratio = train_opt['D_update_ratio'] if train_opt['D_update_ratio'] else 1
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self.D_init_iters = train_opt['D_init_iters'] if train_opt['D_init_iters'] else 0
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self.G_warmup = train_opt['G_warmup'] if train_opt['G_warmup'] else 0
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self.G_warmup = train_opt['G_warmup'] if train_opt['G_warmup'] else -1
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self.D_noise_theta = train_opt['D_noise_theta_init'] if train_opt['D_noise_theta_init'] else 0
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self.D_noise_final = train_opt['D_noise_final_it'] if train_opt['D_noise_final_it'] else 0
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self.D_noise_theta_floor = train_opt['D_noise_theta_floor'] if train_opt['D_noise_theta_floor'] else 0
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@ -200,6 +200,8 @@ class SRGANModel(BaseModel):
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# GAN LQ image params
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self.gan_lq_img_use_prob = train_opt['gan_lowres_use_probability'] if train_opt['gan_lowres_use_probability'] else 0
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self.img_debug_steps = train_opt['img_debug_steps'] if train_opt['img_debug_steps'] else 50
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self.print_network() # print network
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self.load() # load G and D if needed
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self.load_random_corruptor()
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@ -356,7 +358,7 @@ class SRGANModel(BaseModel):
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_t = time()
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# D
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if self.l_gan_w > 0 and step > self.G_warmup:
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if self.l_gan_w > 0 and step >= self.G_warmup:
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for p in self.netD.parameters():
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if p.dtype != torch.int64 and p.dtype != torch.bool:
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p.requires_grad = True
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@ -506,7 +508,7 @@ class SRGANModel(BaseModel):
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_t = time()
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# Log sample images from first microbatch.
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if step % 50 == 0:
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if step % self.img_debug_steps == 0:
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sample_save_path = os.path.join(self.opt['path']['models'], "..", "temp")
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os.makedirs(os.path.join(sample_save_path, "hr"), exist_ok=True)
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os.makedirs(os.path.join(sample_save_path, "lr"), exist_ok=True)
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@ -524,7 +526,7 @@ class SRGANModel(BaseModel):
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utils.save_image(self.pix[i].cpu(), os.path.join(sample_save_path, "pix", "%05i_%02i.png" % (step, i)))
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utils.save_image(self.fake_GenOut[i].cpu(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i)))
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utils.save_image(self.fea_GenOut[i].cpu(), os.path.join(sample_save_path, "gen_fea", "%05i_%02i.png" % (step, i)))
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if self.l_gan_w > 0 and step > self.G_warmup and 'pixgan' in self.opt['train']['gan_type']:
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if self.l_gan_w > 0 and step >= self.G_warmup and 'pixgan' in self.opt['train']['gan_type']:
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utils.save_image(var_ref_skips[i].cpu(), os.path.join(sample_save_path, "ref", "%05i_%02i.png" % (step, i)))
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utils.save_image(self.fake_H[i], os.path.join(sample_save_path, "disc_fake", "fake%05i_%02i.png" % (step, i)))
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utils.save_image(F.interpolate(fake_disc_images[i], scale_factor=4), os.path.join(sample_save_path, "disc", "fake%05i_%02i.png" % (step, i)))
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@ -545,7 +547,7 @@ class SRGANModel(BaseModel):
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self.add_log_entry('l_d_fea_real', l_d_fea_real.item() * self.mega_batch_factor)
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self.add_log_entry('l_d_fake_total', l_d_fake.item() * self.mega_batch_factor)
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self.add_log_entry('l_d_real_total', l_d_real.item() * self.mega_batch_factor)
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if self.l_gan_w > 0 and step > self.G_warmup:
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if self.l_gan_w > 0 and step >= self.G_warmup:
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self.add_log_entry('l_d_real', l_d_real_log.item())
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self.add_log_entry('l_d_fake', l_d_fake_log.item())
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self.add_log_entry('D_fake', torch.mean(pred_d_fake.detach()))
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