Pierrotlc's group workspace
Group: ADAIN - 32x32
State
Notes
User
Tags
Created
Runtime
Sweep
batch_size
betas_d
betas_g
device
dim_image
dim_z
dropout
epochs
iter_D
lr_d
lr_g
n_channels
n_first_channels
n_layers_d_block
n_layers_z
netD
netG
optimD
optimG
seed
test_loader
train_loader
weight_GP
weight_err_d_real
data_cfg.dim_image
data_cfg.path
dataloader
discriminator_cfg.betas
discriminator_cfg.dropout
discriminator_cfg.gamma
discriminator_cfg.lr
discriminator_cfg.n_channels
discriminator_cfg.n_layers_d_block
discriminator_cfg.running_avg_factor
discriminator_cfg.weight_avg_factor
gamma_d
gamma_g
generator_cfg.betas
generator_cfg.dim_z
generator_cfg.dropout
generator_cfg.gamma
generator_cfg.lr
generator_cfg.n_channels
generator_cfg.n_layers_block
Finished
-
pierrotlc
34m 52s
-
256
[0.5,0.9]
[0.5,0.5]
cuda
32
32
0.3
200
-
0.0001
0.00001
256
12
5
4
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(blocks): ModuleList(
(0): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(12, 24, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(1): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(24, 48, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(2): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(48, 96, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(3): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(96, 192, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(4): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(192, 384, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
)
(classify): Sequential(
(0): Conv2d(384, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): Flatten(start_dim=1, end_dim=-1)
)
)
StyleGAN(
(mapping): MappingNetwork(
(norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(out): Linear(in_features=32, out_features=32, bias=True)
)
(synthesis): SynthesisNetwork(
(blocks): ModuleList(
(0): SynthesisBlock(
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=512, bias=True)
(A2): Linear(in_features=32, out_features=512, bias=True)
(B1): Conv2d(10, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): SynthesisBlock(
(upsample): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=256, bias=True)
(A2): Linear(in_features=32, out_features=256, bias=True)
(B1): Conv2d(10, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): SynthesisBlock(
(upsample): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=128, bias=True)
(A2): Linear(in_features=32, out_features=128, bias=True)
(B1): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): SynthesisBlock(
(upsample): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=64, bias=True)
(A2): Linear(in_features=32, out_features=64, bias=True)
(B1): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): SynthesisBlock(
(upsample): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=32, bias=True)
(A2): Linear(in_features=32, out_features=32, bias=True)
(B1): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(to_rgb): Conv2d(16, 3, kernel_size=(1, 1), stride=(1, 1))
)
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.9)
eps: 1e-08
initial_lr: 0.0001
lr: 0.0001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.5)
eps: 1e-08
initial_lr: 1e-05
lr: 1e-05
weight_decay: 0
)
0
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<torch.utils.data.dataloader.DataLoader object at 0x7fe42009dd60>
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0.9
0.9
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Failed
-
pierrotlc
11m 17s
-
256
[0.5,0.9]
[0.5,0.5]
cuda
32
32
0.3
200
-
0.01
0.001
256
12
5
4
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(blocks): ModuleList(
(0): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(12, 24, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(1): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(24, 48, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(2): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(48, 96, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(3): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(96, 192, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(4): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(192, 384, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
)
(classify): Sequential(
(0): Conv2d(384, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): Flatten(start_dim=1, end_dim=-1)
)
)
StyleGAN(
(mapping): MappingNetwork(
(norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(out): Linear(in_features=32, out_features=32, bias=True)
)
(synthesis): SynthesisNetwork(
(blocks): ModuleList(
(0): SynthesisBlock(
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=512, bias=True)
(A2): Linear(in_features=32, out_features=512, bias=True)
(B1): Conv2d(10, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): SynthesisBlock(
(upsample): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=256, bias=True)
(A2): Linear(in_features=32, out_features=256, bias=True)
(B1): Conv2d(10, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): SynthesisBlock(
(upsample): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=128, bias=True)
(A2): Linear(in_features=32, out_features=128, bias=True)
(B1): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): SynthesisBlock(
(upsample): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=64, bias=True)
(A2): Linear(in_features=32, out_features=64, bias=True)
(B1): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): SynthesisBlock(
(upsample): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=32, bias=True)
(A2): Linear(in_features=32, out_features=32, bias=True)
(B1): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(to_rgb): Conv2d(16, 3, kernel_size=(1, 1), stride=(1, 1))
)
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.9)
eps: 1e-08
initial_lr: 0.01
lr: 0.01
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.5)
eps: 1e-08
initial_lr: 0.001
lr: 0.001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7f0c91aebd60>
-
-
-
-
-
-
-
-
0.9
0.9
-
-
-
-
-
-
-
Failed
-
pierrotlc
27m 3s
-
256
[0.5,0.7]
[0.5,0.5]
cuda
32
32
0.3
50
-
0.01
0.001
256
12
5
4
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(blocks): ModuleList(
(0): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(12, 24, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(1): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(24, 48, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(2): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(48, 96, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(3): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(96, 192, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(4): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(192, 384, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
)
(classify): Sequential(
(0): Conv2d(384, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): Flatten(start_dim=1, end_dim=-1)
)
)
StyleGAN(
(mapping): MappingNetwork(
(norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(out): Linear(in_features=32, out_features=32, bias=True)
)
(synthesis): SynthesisNetwork(
(blocks): ModuleList(
(0): SynthesisBlock(
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=512, bias=True)
(A2): Linear(in_features=32, out_features=512, bias=True)
(B1): Conv2d(10, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): SynthesisBlock(
(upsample): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=256, bias=True)
(A2): Linear(in_features=32, out_features=256, bias=True)
(B1): Conv2d(10, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): SynthesisBlock(
(upsample): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=128, bias=True)
(A2): Linear(in_features=32, out_features=128, bias=True)
(B1): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): SynthesisBlock(
(upsample): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=64, bias=True)
(A2): Linear(in_features=32, out_features=64, bias=True)
(B1): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): SynthesisBlock(
(upsample): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=32, bias=True)
(A2): Linear(in_features=32, out_features=32, bias=True)
(B1): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(to_rgb): Conv2d(16, 3, kernel_size=(1, 1), stride=(1, 1))
)
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.7)
eps: 1e-08
initial_lr: 0.01
lr: 0.01
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.5)
eps: 1e-08
initial_lr: 0.001
lr: 0.001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7efeaab5ed30>
-
-
-
-
-
-
-
-
0.9
0.9
-
-
-
-
-
-
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Failed
-
pierrotlc
1h 1m 21s
-
256
[0.5,0.99]
[0.5,0.5]
cuda
32
32
0.3
50
-
0.001
0.0001
128
8
5
4
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(blocks): ModuleList(
(0): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(8, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(1): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(2): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(3): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(4): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
)
(classify): Sequential(
(0): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): Flatten(start_dim=1, end_dim=-1)
)
)
StyleGAN(
(mapping): MappingNetwork(
(norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(out): Linear(in_features=32, out_features=32, bias=True)
)
(synthesis): SynthesisNetwork(
(blocks): ModuleList(
(0): SynthesisBlock(
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=256, bias=True)
(A2): Linear(in_features=32, out_features=256, bias=True)
(B1): Conv2d(10, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): SynthesisBlock(
(upsample): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=128, bias=True)
(A2): Linear(in_features=32, out_features=128, bias=True)
(B1): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): SynthesisBlock(
(upsample): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=64, bias=True)
(A2): Linear(in_features=32, out_features=64, bias=True)
(B1): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): SynthesisBlock(
(upsample): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=32, bias=True)
(A2): Linear(in_features=32, out_features=32, bias=True)
(B1): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): SynthesisBlock(
(upsample): ConvTranspose2d(16, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=16, bias=True)
(A2): Linear(in_features=32, out_features=16, bias=True)
(B1): Conv2d(10, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(to_rgb): Conv2d(8, 3, kernel_size=(1, 1), stride=(1, 1))
)
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.99)
eps: 1e-08
initial_lr: 0.001
lr: 0.001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.5)
eps: 1e-08
initial_lr: 0.0001
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7f96b846a070>
-
-
-
-
-
-
-
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0.9
0.9
-
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-
-
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Failed
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pierrotlc
29m 22s
-
256
[0.5,0.99]
[0.5,0.5]
cuda
32
32
0.3
50
-
0.001
0.0001
128
8
5
4
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(blocks): ModuleList(
(0): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(8, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(1): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(2): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(3): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(4): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
)
(classify): Sequential(
(0): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): Flatten(start_dim=1, end_dim=-1)
)
)
StyleGAN(
(mapping): MappingNetwork(
(norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(out): Linear(in_features=32, out_features=32, bias=True)
)
(synthesis): SynthesisNetwork(
(blocks): ModuleList(
(0): SynthesisBlock(
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=256, bias=True)
(A2): Linear(in_features=32, out_features=256, bias=True)
(B1): Conv2d(10, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): SynthesisBlock(
(upsample): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=128, bias=True)
(A2): Linear(in_features=32, out_features=128, bias=True)
(B1): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): SynthesisBlock(
(upsample): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=64, bias=True)
(A2): Linear(in_features=32, out_features=64, bias=True)
(B1): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): SynthesisBlock(
(upsample): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=32, bias=True)
(A2): Linear(in_features=32, out_features=32, bias=True)
(B1): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): SynthesisBlock(
(upsample): ConvTranspose2d(16, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=16, bias=True)
(A2): Linear(in_features=32, out_features=16, bias=True)
(B1): Conv2d(10, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(to_rgb): Conv2d(8, 3, kernel_size=(1, 1), stride=(1, 1))
)
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.99)
eps: 1e-08
initial_lr: 0.001
lr: 0.001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.5)
eps: 1e-08
initial_lr: 0.0001
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7f60001a8100>
-
-
-
-
-
-
-
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0.9
0.9
-
-
-
-
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Finished
-
pierrotlc
37m 12s
-
256
[0.5,0.99]
[0.5,0.5]
cuda
32
32
0.3
50
-
0.0001
0.0001
128
8
5
4
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(blocks): ModuleList(
(0): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(8, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(1): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(2): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(3): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(4): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
)
(classify): Sequential(
(0): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): Flatten(start_dim=1, end_dim=-1)
)
)
StyleGAN(
(mapping): MappingNetwork(
(norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(out): Linear(in_features=32, out_features=32, bias=True)
)
(synthesis): SynthesisNetwork(
(blocks): ModuleList(
(0): SynthesisBlock(
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=256, bias=True)
(A2): Linear(in_features=32, out_features=256, bias=True)
(B1): Conv2d(10, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): SynthesisBlock(
(upsample): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=128, bias=True)
(A2): Linear(in_features=32, out_features=128, bias=True)
(B1): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): SynthesisBlock(
(upsample): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=64, bias=True)
(A2): Linear(in_features=32, out_features=64, bias=True)
(B1): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): SynthesisBlock(
(upsample): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=32, bias=True)
(A2): Linear(in_features=32, out_features=32, bias=True)
(B1): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): SynthesisBlock(
(upsample): ConvTranspose2d(16, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(layers): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(4): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(5): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(6): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(7): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=16, bias=True)
(A2): Linear(in_features=32, out_features=16, bias=True)
(B1): Conv2d(10, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(to_rgb): Conv2d(8, 3, kernel_size=(1, 1), stride=(1, 1))
)
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.99)
eps: 1e-08
initial_lr: 0.0001
lr: 0.0001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.5)
eps: 1e-08
initial_lr: 0.0001
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7fb048cb8070>
-
-
-
-
-
-
-
-
0.9
0.9
-
-
-
-
-
-
-
Failed
-
pierrotlc
21m 44s
-
64
[0.5,0.99]
[0.5,0.5]
cuda
32
32
0.3
50
-
0.001
0.001
64
4
2
4
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(blocks): ModuleList(
(0): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(4, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(1): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(8, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(2): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(3): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(4): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
)
(classify): Sequential(
(0): Conv2d(128, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): Flatten(start_dim=1, end_dim=-1)
)
)
StyleGAN(
(mapping): MappingNetwork(
(norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(out): Linear(in_features=32, out_features=32, bias=True)
)
(synthesis): SynthesisNetwork(
(blocks): ModuleList(
(0): SynthesisBlock(
(conv2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=128, bias=True)
(A2): Linear(in_features=32, out_features=128, bias=True)
(B1): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): SynthesisBlock(
(upsample): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(conv2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=64, bias=True)
(A2): Linear(in_features=32, out_features=64, bias=True)
(B1): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): SynthesisBlock(
(upsample): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(conv2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=32, bias=True)
(A2): Linear(in_features=32, out_features=32, bias=True)
(B1): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): SynthesisBlock(
(upsample): ConvTranspose2d(16, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(conv2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=16, bias=True)
(A2): Linear(in_features=32, out_features=16, bias=True)
(B1): Conv2d(10, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): SynthesisBlock(
(upsample): ConvTranspose2d(8, 4, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(conv2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=8, bias=True)
(A2): Linear(in_features=32, out_features=8, bias=True)
(B1): Conv2d(10, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(to_rgb): Conv2d(4, 3, kernel_size=(1, 1), stride=(1, 1))
)
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.99)
eps: 1e-08
initial_lr: 0.001
lr: 0.001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.5)
eps: 1e-08
initial_lr: 0.001
lr: 0.001
weight_decay: 0
)
0
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<torch.utils.data.dataloader.DataLoader object at 0x7f3259bfc520>
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0.65
0.5
-
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-
-
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Failed
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pierrotlc
16m 12s
-
64
[0.5,0.99]
[0.5,0.5]
cuda
32
32
0.3
50
-
0.0001
0.0001
64
4
2
4
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(blocks): ModuleList(
(0): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(4, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(1): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(8, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(2): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(3): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
(4): DiscriminatorBlock(
(convs): ModuleList(
(0): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
)
(classify): Sequential(
(0): Conv2d(128, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): Flatten(start_dim=1, end_dim=-1)
)
)
StyleGAN(
(mapping): MappingNetwork(
(norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(out): Linear(in_features=32, out_features=32, bias=True)
)
(synthesis): SynthesisNetwork(
(blocks): ModuleList(
(0): SynthesisBlock(
(conv2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=128, bias=True)
(A2): Linear(in_features=32, out_features=128, bias=True)
(B1): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): SynthesisBlock(
(upsample): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(conv2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=64, bias=True)
(A2): Linear(in_features=32, out_features=64, bias=True)
(B1): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): SynthesisBlock(
(upsample): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(conv2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=32, bias=True)
(A2): Linear(in_features=32, out_features=32, bias=True)
(B1): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): SynthesisBlock(
(upsample): ConvTranspose2d(16, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(conv2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=16, bias=True)
(A2): Linear(in_features=32, out_features=16, bias=True)
(B1): Conv2d(10, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): SynthesisBlock(
(upsample): ConvTranspose2d(8, 4, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv1): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(conv2): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
(A1): Linear(in_features=32, out_features=8, bias=True)
(A2): Linear(in_features=32, out_features=8, bias=True)
(B1): Conv2d(10, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(B2): Conv2d(10, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(to_rgb): Conv2d(4, 3, kernel_size=(1, 1), stride=(1, 1))
)
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.99)
eps: 1e-08
initial_lr: 0.0001
lr: 0.0001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.5)
eps: 1e-08
initial_lr: 0.0001
lr: 0.0001
weight_decay: 0
)
0
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-
-
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-
<torch.utils.data.dataloader.DataLoader object at 0x7f3dc8857520>
-
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-
-
-
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0.65
0.5
-
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1-8
of 8