forked from mrq/DL-Art-School
Support >128px image squares
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@ -4,7 +4,8 @@ import torchvision
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class Discriminator_VGG_128(nn.Module):
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def __init__(self, in_nc, nf):
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# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
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def __init__(self, in_nc, nf, input_img_factor=1):
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super(Discriminator_VGG_128, self).__init__()
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# [64, 128, 128]
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self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
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@ -31,7 +32,7 @@ class Discriminator_VGG_128(nn.Module):
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self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True)
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self.linear1 = nn.Linear(512 * 4 * 4, 100)
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self.linear1 = nn.Linear(512 * 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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@ -32,11 +32,12 @@ def define_G(opt):
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# Discriminator
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def define_D(opt):
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img_sz = opt['datasets']['train']['GT_size']
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opt_net = opt['network_D']
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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'])
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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)
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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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