Merge pull request #7 from FeepingCreature/labels-instead-of-index
Instead of manually counting with str(i), use unique labels for dcgan
This commit is contained in:
martinarjovsky
GitHub
commit
040d553d2a
121
models/dcgan.py
121
models/dcgan.py
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@ -8,38 +8,37 @@ class DCGAN_D(nn.Container):
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self.ngpu = ngpu
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assert isize % 16 == 0, "isize has to be a multiple of 16"
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main = nn.Sequential(
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# input is nc x isize x isize
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nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
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nn.LeakyReLU(0.2, inplace=True),
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)
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i, csize, cndf = 2, isize / 2, ndf
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main = nn.Sequential()
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# input is nc x isize x isize
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main.add_module('initial.conv.{0}-{1}'.format(nc, ndf),
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nn.Conv2d(nc, ndf, 4, 2, 1, bias=False))
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main.add_module('initial.relu.{0}'.format(ndf),
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nn.LeakyReLU(0.2, inplace=True))
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csize, cndf = isize / 2, ndf
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# Extra layers
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for t in range(n_extra_layers):
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main.add_module(str(i),
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main.add_module('extra-layers-{0}.{1}.conv'.format(t, cndf),
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nn.Conv2d(cndf, cndf, 3, 1, 1, bias=False))
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main.add_module(str(i+1),
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main.add_module('extra-layers-{0}.{1}.batchnorm'.format(t, cndf),
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nn.BatchNorm2d(cndf))
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main.add_module(str(i+2),
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main.add_module('extra-layers-{0}.{1}.relu'.format(t, cndf),
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nn.LeakyReLU(0.2, inplace=True))
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i += 3
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while csize > 4:
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in_feat = cndf
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out_feat = cndf * 2
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main.add_module(str(i),
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main.add_module('pyramid.{0}-{1}.conv'.format(in_feat, out_feat),
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nn.Conv2d(in_feat, out_feat, 4, 2, 1, bias=False))
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main.add_module(str(i+1),
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main.add_module('pyramid.{0}.batchnorm'.format(out_feat),
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nn.BatchNorm2d(out_feat))
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main.add_module(str(i+2),
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main.add_module('pyramid.{0}.relu'.format(out_feat),
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nn.LeakyReLU(0.2, inplace=True))
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i+=3
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cndf = cndf * 2
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csize = csize / 2
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# state size. K x 4 x 4
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main.add_module(str(i),
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main.add_module('final.{0}-{1}.conv'.format(cndf, 1),
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nn.Conv2d(cndf, 1, 4, 1, 0, bias=False))
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self.main = main
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@ -63,38 +62,39 @@ class DCGAN_G(nn.Container):
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cngf = cngf * 2
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tisize = tisize * 2
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main = nn.Sequential(
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# input is Z, going into a convolution
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nn.ConvTranspose2d(nz, cngf, 4, 1, 0, bias=False),
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nn.BatchNorm2d(cngf),
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nn.ReLU(True),
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)
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main = nn.Sequential()
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# input is Z, going into a convolution
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main.add_module('initial.{0}-{1}.convt'.format(nz, cngf),
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nn.ConvTranspose2d(nz, cngf, 4, 1, 0, bias=False))
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main.add_module('initial.{0}.batchnorm'.format(cngf),
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nn.BatchNorm2d(cngf))
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main.add_module('initial.{0}.relu'.format(cngf),
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nn.ReLU(True))
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i, csize, cndf = 3, 4, cngf
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csize, cndf = 4, cngf
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while csize < isize//2:
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main.add_module(str(i),
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nn.ConvTranspose2d(cngf, cngf//2, 4, 2, 1, bias=False))
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main.add_module(str(i+1),
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main.add_module('pyramid.{0}-{1}.convt'.format(cngf, cngf//2),
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nn.ConvTranspose2d(cngf, cngf//2, 4, 2, 1, bias=False))
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main.add_module('pyramid.{0}.batchnorm'.format(cngf//2),
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nn.BatchNorm2d(cngf//2))
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main.add_module(str(i+2),
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main.add_module('pyramid.{0}.relu'.format(cngf//2),
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nn.ReLU(True))
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i += 3
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cngf = cngf // 2
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csize = csize * 2
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# Extra layers
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for t in range(n_extra_layers):
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main.add_module(str(i),
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main.add_module('extra-layers-{0}.{1}.conv'.format(t, cngf),
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nn.Conv2d(cngf, cngf, 3, 1, 1, bias=False))
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main.add_module(str(i+1),
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main.add_module('extra-layers-{0}.{1}.batchnorm'.format(t, cngf),
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nn.BatchNorm2d(cngf))
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main.add_module(str(i+2),
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main.add_module('extra-layers-{0}.{1}.relu'.format(t, cngf),
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nn.ReLU(True))
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i += 3
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main.add_module(str(i),
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main.add_module('final.{0}-{1}.convt'.format(cngf, nc),
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nn.ConvTranspose2d(cngf, nc, 4, 2, 1, bias=False))
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main.add_module(str(i+1), nn.Tanh())
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main.add_module('final.{0}.tanh'.format(nc),
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nn.Tanh())
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self.main = main
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def forward(self, input):
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@ -110,34 +110,34 @@ class DCGAN_D_nobn(nn.Container):
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self.ngpu = ngpu
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assert isize % 16 == 0, "isize has to be a multiple of 16"
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main = nn.Sequential(
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# input is nc x isize x isize
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nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
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nn.LeakyReLU(0.2, inplace=True),
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)
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i, csize, cndf = 2, isize / 2, ndf
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main = nn.Sequential()
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# input is nc x isize x isize
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# input is nc x isize x isize
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main.add_module('initial.conv.{0}-{1}'.format(nc, ndf),
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nn.Conv2d(nc, ndf, 4, 2, 1, bias=False))
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main.add_module('initial.relu.{0}'.format(ndf),
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nn.LeakyReLU(0.2, inplace=True))
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csize, cndf = isize / 2, ndf
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# Extra layers
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for t in range(n_extra_layers):
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main.add_module(str(i),
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main.add_module('extra-layers-{0}.{1}.conv'.format(t, cndf),
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nn.Conv2d(cndf, cndf, 3, 1, 1, bias=False))
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main.add_module(str(i+1),
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main.add_module('extra-layers-{0}.{1}.relu'.format(t, cndf),
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nn.LeakyReLU(0.2, inplace=True))
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i += 2
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while csize > 4:
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in_feat = cndf
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out_feat = cndf * 2
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main.add_module(str(i),
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main.add_module('pyramid.{0}-{1}.conv'.format(in_feat, out_feat),
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nn.Conv2d(in_feat, out_feat, 4, 2, 1, bias=False))
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main.add_module(str(i+1),
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main.add_module('pyramid.{0}.relu'.format(out_feat),
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nn.LeakyReLU(0.2, inplace=True))
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i+=2
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cndf = cndf * 2
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csize = csize / 2
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# state size. K x 4 x 4
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main.add_module(str(i),
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main.add_module('final.{0}-{1}.conv'.format(cndf, 1),
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nn.Conv2d(cndf, 1, 4, 1, 0, bias=False))
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self.main = main
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@ -161,33 +161,32 @@ class DCGAN_G_nobn(nn.Container):
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cngf = cngf * 2
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tisize = tisize * 2
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main = nn.Sequential(
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# input is Z, going into a convolution
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nn.ConvTranspose2d(nz, cngf, 4, 1, 0, bias=False),
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nn.ReLU(True),
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)
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main = nn.Sequential()
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main.add_module('initial.{0}-{1}.convt'.format(nz, cngf),
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nn.ConvTranspose2d(nz, cngf, 4, 1, 0, bias=False))
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main.add_module('initial.{0}.relu'.format(cngf),
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nn.ReLU(True))
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i, csize, cndf = 3, 4, cngf
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csize, cndf = 4, cngf
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while csize < isize//2:
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main.add_module(str(i),
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nn.ConvTranspose2d(cngf, cngf//2, 4, 2, 1, bias=False))
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main.add_module(str(i+1),
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main.add_module('pyramid.{0}-{1}.convt'.format(cngf, cngf//2),
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nn.ConvTranspose2d(cngf, cngf//2, 4, 2, 1, bias=False))
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main.add_module('pyramid.{0}.relu'.format(cngf//2),
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nn.ReLU(True))
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i += 2
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cngf = cngf // 2
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csize = csize * 2
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# Extra layers
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for t in range(n_extra_layers):
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main.add_module(str(i),
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main.add_module('extra-layers-{0}.{1}.conv'.format(t, cngf),
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nn.Conv2d(cngf, cngf, 3, 1, 1, bias=False))
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main.add_module(str(i+1),
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main.add_module('extra-layers-{0}.{1}.relu'.format(t, cngf),
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nn.ReLU(True))
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i += 2
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main.add_module(str(i),
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main.add_module('final.{0}-{1}.convt'.format(cngf, nc),
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nn.ConvTranspose2d(cngf, nc, 4, 2, 1, bias=False))
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main.add_module(str(i+1), nn.Tanh())
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main.add_module('final.{0}.tanh'.format(nc),
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nn.Tanh())
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self.main = main
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def forward(self, input):
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