forked from mrq/DL-Art-School
wrap disc grad
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@ -77,7 +77,7 @@ class SRGANModel(BaseModel):
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if self.is_train:
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self.netD = networks.define_D(opt).to(self.device)
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if self.spsr_enabled:
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self.netD_grad = networks.define_D(opt).to(self.device) # D_grad
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self.netD_grad = networks.define_D(opt, wrap=True).to(self.device) # D_grad
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if 'network_C' in opt.keys():
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self.netC = networks.define_G(opt, net_key='network_C').to(self.device)
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@ -140,7 +140,15 @@ def define_G(opt, net_key='network_G', scale=None):
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return netG
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def define_D_net(opt_net, img_sz=None):
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class GradDiscWrapper(torch.nn.Module):
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def __init__(self, m):
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super(GradDiscWrapper, self).__init__()
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self.m = m
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def forward(self, x, lr):
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return self.m(x, lr)
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def define_D_net(opt_net, img_sz=None, wrap=False):
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which_model = opt_net['which_model_D']
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if which_model == 'discriminator_vgg_128':
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@ -164,15 +172,17 @@ def define_D_net(opt_net, img_sz=None):
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final_temperature_step=opt_net['final_temperature_step'])
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elif which_model == "cross_compare_vgg128":
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netD = SRGAN_arch.CrossCompareDiscriminator(in_nc=opt_net['in_nc'], ref_channels=opt_net['ref_channels'], nf=opt_net['nf'], scale=opt_net['scale'])
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if wrap:
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netD = GradDiscWrapper(netD)
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else:
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raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))
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return netD
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# Discriminator
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def define_D(opt):
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def define_D(opt, wrap=False):
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img_sz = opt['datasets']['train']['target_size']
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opt_net = opt['network_D']
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return define_D_net(opt_net, img_sz)
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return define_D_net(opt_net, img_sz, wrap=wrap)
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def define_fixed_D(opt):
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# Note that this will not work with "old" VGG-style discriminators with dense blocks until the img_size parameter is added.
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