code stringlengths 3 6.57k |
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super(tdecode_ResidualBlock, self) |
__init__() |
nn.ConvTranspose2d(in_channels=8*in_features,out_channels=4*in_features,kernel_size=(3, 3) |
nn.BatchNorm2d(4*in_features) |
nn.LeakyReLU(inplace=True) |
nn.ConvTranspose2d(in_channels=4*in_features,out_channels=1*in_features,kernel_size=(3, 3) |
nn.BatchNorm2d(1*in_features) |
nn.LeakyReLU(inplace=True) |
forward(self, encode_x) |
self.tdecode_block(encode_x) |
F.sigmoid(decode_x) |
target_encode_Generator(nn.Module) |
__init__(self) |
super(target_encode_Generator, self) |
__init__() |
nn.Linear(opt.latent_dim, opt.channels*opt.img_size**2) |
nn.Sequential(nn.Conv2d(opt.channels*2, 64, 3, 1, 1) |
nn.ReLU(inplace=True) |
range(opt.n_residual_blocks) |
resblocks.append(tencode_ResidualBlock() |
nn.Sequential(*resblocks) |
forward(self, img, z) |
torch.cat((img, self.tfc(z) |
view(*img.shape) |
self.tl1(gen_input) |
self.tencode_resblocks(out) |
source_encode_Generator(nn.Module) |
__init__(self) |
super(source_encode_Generator, self) |
__init__() |
nn.Linear(opt.latent_dim, opt.channels*opt.img_size**2) |
nn.Sequential(nn.Conv2d(opt.channels*2, 64, 3, 1, 1) |
nn.ReLU(inplace=True) |
range(opt.n_residual_blocks) |
resblocks.append(sencode_ResidualBlock() |
nn.Sequential(*resblocks) |
forward(self, img, z) |
torch.cat((img, self.sfc(z) |
view(*img.shape) |
self.sl1(gen_input) |
self.sencode_resblocks(out) |
target_decode_Generator(nn.Module) |
__init__(self) |
super(target_decode_Generator, self) |
__init__() |
range(opt.n_residual_blocks) |
resblocks.append(tdecode_ResidualBlock() |
nn.Sequential(*resblocks) |
nn.Sequential(nn.Conv2d(64, opt.channels, 3, 1, 1) |
nn.Tanh() |
forward(self, img, encode_out) |
self.target_decode_resblocks(encode_out) |
self.tl2(out) |
source_decode_Generator(nn.Module) |
__init__(self) |
super(source_decode_Generator, self) |
__init__() |
range(opt.n_residual_blocks) |
resblocks.append(sdecode_ResidualBlock() |
nn.Sequential(*resblocks) |
nn.Sequential(nn.Conv2d(64, opt.channels, 3, 1, 1) |
nn.Tanh() |
forward(self, img, encode_out) |
self.source_decode_resblocks(encode_out) |
self.sl2(out) |
encode_Discriminator(nn.Module) |
__init__(self) |
super(encode_Discriminator, self) |
__init__() |
block(in_features, out_features, normalization=True) |
nn.Conv2d(in_features, out_features, 3, stride=2, padding=1) |
nn.LeakyReLU(0.2, inplace=True) |
layers.append(nn.InstanceNorm2d(out_features) |
block(256, 512, normalization=False) |
block(512, 1024) |
nn.Conv2d(1024, 1, 3, 1, 1) |
forward(self, encode_x) |
self.model(encode_x) |
Discriminator(nn.Module) |
__init__(self) |
super(Discriminator, self) |
__init__() |
block(in_features, out_features, normalization=True) |
nn.Conv2d(in_features, out_features, 3, stride=2, padding=1) |
nn.LeakyReLU(0.2, inplace=True) |
layers.append(nn.InstanceNorm2d(out_features) |
block(opt.channels, 64, normalization=False) |
block(64, 128) |
block(128, 256) |
block(256, 512) |
nn.Conv2d(512, 1, 3, 1, 1) |
forward(self, img) |
self.model(img) |
encode_Classifier(nn.Module) |
__init__(self) |
super(encode_Classifier, self) |
__init__() |
block(in_features, out_features, normalization=True) |
nn.Conv2d(in_features, out_features, 3, stride=2, padding=1) |
nn.LeakyReLU(0.2, inplace=True) |
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