Fix vgg_gn input_img_factor
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@ -108,7 +108,7 @@ class Discriminator_VGG_128_GN(nn.Module):
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self.bn4_1 = nn.GroupNorm(8, nf * 8, affine=True)
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final_nf = nf * 8
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self.linear1 = nn.Linear(final_nf * 4 * input_img_factor * 4 * input_img_factor, 100)
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self.linear1 = nn.Linear(int(final_nf * 4 * input_img_factor * 4 * input_img_factor), 100)
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self.linear2 = nn.Linear(100, 1)
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# activation function
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@ -160,9 +160,9 @@ 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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netD = SRGAN_arch.Discriminator_VGG_128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz // 128, extra_conv=opt_net['extra_conv'])
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netD = SRGAN_arch.Discriminator_VGG_128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128, extra_conv=opt_net['extra_conv'])
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elif which_model == 'discriminator_vgg_128_gn':
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netD = SRGAN_arch.Discriminator_VGG_128_GN(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz // 128)
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netD = SRGAN_arch.Discriminator_VGG_128_GN(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128)
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if wrap:
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netD = GradDiscWrapper(netD)
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elif which_model == 'discriminator_resnet':
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@ -32,7 +32,7 @@ def init_dist(backend='nccl', **kwargs):
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def main():
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#### options
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/srgan_compute_feature.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/pretrain_spsr_switched2_psnr.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
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help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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