Pierrotlc's group workspace
Group: Alternating - 32x32
Name
14 visualized
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
38m 28s
-
256
[0.5,0.99]
[0.7,0.99]
cuda
32
64
0.3
50
-
0.001
0.0001
64
4
2
2
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((64,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0): Sequential(
(0): Linear(in_features=64, out_features=64, bias=True)
(1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=64, out_features=64, bias=True)
(1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(out): Linear(in_features=64, out_features=64, 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()
)
(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()
)
(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()
)
(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()
)
)
(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
lr: 0.001
weight_decay: 0.01
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.7, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0.01
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7f816104fac0>
-
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Finished
pierrotlc
38m 13s
-
256
[0.5,0.99]
[0.7,0.99]
cuda
32
64
0.3
50
-
0.001
0.0001
64
4
2
2
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((64,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0): Sequential(
(0): Linear(in_features=64, out_features=64, bias=True)
(1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=64, out_features=64, bias=True)
(1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(out): Linear(in_features=64, out_features=64, 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()
)
(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()
)
(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()
)
(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()
)
)
(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
lr: 0.001
weight_decay: 0.001
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.7, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0.001
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7fdad3577a90>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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Failed
pierrotlc
36m 48s
-
256
[0.5,0.99]
[0.7,0.99]
cuda
32
32
0.3
100
-
0.0001
0.00001
64
4
2
2
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)
)
)
(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()
)
(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()
)
(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()
)
(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()
)
)
(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
lr: 0.0001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.7, 0.99)
eps: 1e-08
lr: 1e-05
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7f99600afa60>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Failed
pierrotlc
1h 5m 30s
-
256
[0.5,0.99]
[0.7,0.99]
cuda
32
32
0.3
100
-
0.0005
0.0001
64
4
2
2
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)
)
)
(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()
)
(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()
)
(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()
)
(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()
)
)
(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
lr: 0.0005
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.7, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7f7df5169ac0>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Finished
pierrotlc
1h 10m 2s
-
256
[0.5,0.99]
[0.8,0.99]
cuda
32
32
0.3
100
-
0.0001
0.0001
64
4
2
2
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)
)
)
(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()
)
(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()
)
(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()
)
(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()
)
)
(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
lr: 0.0001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.8, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7f13f672ca90>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Failed
pierrotlc
42m 3s
-
256
[0.7,0.99]
[0.7,0.99]
cuda
32
32
0.3
100
-
0.0001
0.0001
64
4
2
2
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)
)
)
(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()
)
(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()
)
(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()
)
(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()
)
)
(to_rgb): Conv2d(8, 3, kernel_size=(1, 1), stride=(1, 1))
)
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.7, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.7, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7f9871e8fac0>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Failed
pierrotlc
31m 1s
-
256
[0.5,0.99]
[0.7,0.99]
cuda
32
32
0.3
100
-
0.0001
0.0001
64
4
2
2
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)
)
)
(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()
)
(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()
)
(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()
)
(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()
)
)
(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
lr: 0.0001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.7, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7ff7b8aa0ac0>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Failed
pierrotlc
9m 14s
-
256
[0.5,0.99]
[0.5,0.99]
cuda
32
32
0.3
100
-
0.0001
0.0001
64
4
2
2
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)
)
)
(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()
)
(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()
)
(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()
)
(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()
)
)
(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
lr: 0.0001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7f8b8c4daaf0>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Finished
pierrotlc
46m 3s
-
256
[0.5,0.99]
[0.5,0.99]
cuda
32
32
0.3
100
-
0.0001
0.0001
32
2
2
2
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 2, 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(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(2, 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(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(2, 4, 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(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))
)
(2): 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))
)
(3): 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))
)
(4): 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))
)
)
(classify): Sequential(
(0): Conv2d(64, 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)
)
)
(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(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
)
(1): 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()
)
(2): 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()
)
(3): 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()
)
)
(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
lr: 0.0001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7f2a8f7c1af0>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Failed
pierrotlc
18m 48s
-
256
[0.5,0.99]
[0.5,0.99]
cuda
32
32
0.3
100
-
0.0001
0.0001
32
2
2
2
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 2, 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(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(2, 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(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(2, 4, 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(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))
)
(2): 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))
)
(3): 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))
)
(4): 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))
)
)
(classify): Sequential(
(0): Conv2d(64, 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)
)
)
(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(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
)
(1): 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()
)
(2): 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()
)
(3): 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()
)
)
(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
lr: 0.0001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7faa4a00eac0>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Finished
pierrotlc
16m 13s
-
256
[0.5,0.99]
[0.5,0.99]
cuda
32
32
0.3
100
-
0.0001
0.0001
32
2
2
2
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 2, 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(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(2, 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(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(2, 4, 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(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))
)
(2): 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))
)
(3): 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))
)
(4): 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))
)
)
(classify): Sequential(
(0): Conv2d(64, 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)
)
)
(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(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
)
(1): 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()
)
(2): 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()
)
(3): 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()
)
)
(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
lr: 0.0001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7f42f01ddaf0>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Finished
pierrotlc
3s
-
256
[0.5,0.99]
[0.5,0.99]
cuda
32
32
0.3
100
-
0.0001
0.0001
32
2
2
2
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 2, 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(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(2, 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(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(2, 4, 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(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))
)
(2): 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))
)
(3): 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))
)
(4): 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))
)
)
(classify): Sequential(
(0): Conv2d(64, 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)
)
)
(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(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
)
(1): 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()
)
(2): 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()
)
(3): 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()
)
)
(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
lr: 0.0001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7f118032baf0>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Failed
pierrotlc
11m 52s
-
256
[0.5,0.99]
[0.5,0.99]
cuda
32
32
0.3
100
-
0.001
0.0001
32
2
2
2
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 2, 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(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(2, 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(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(2, 4, 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(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))
)
(2): 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))
)
(3): 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))
)
(4): 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))
)
)
(classify): Sequential(
(0): Conv2d(64, 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)
)
)
(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(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
)
(1): 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()
)
(2): 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()
)
(3): 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()
)
)
(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
lr: 0.001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0
)
0
-
-
-
-
-
-
<torch.utils.data.dataloader.DataLoader object at 0x7ff751692af0>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Failed
pierrotlc
24m 11s
-
64
[0.5,0.99]
[0.5,0.99]
cuda
32
32
0.3
100
-
0.001
0.0001
32
2
2
2
Discriminator(
(first_conv): Sequential(
(0): Dropout(p=0.3, inplace=False)
(1): Conv2d(3, 2, 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(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(2, 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(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(downsample): Conv2d(2, 4, 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(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))
)
(2): 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))
)
(3): 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))
)
(4): 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))
)
)
(classify): Sequential(
(0): Conv2d(64, 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)
)
)
(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(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): LeakyReLU(negative_slope=0.01)
)
(ada_in): AdaIN()
)
(1): 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()
)
(2): 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()
)
(3): 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()
)
)
(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
lr: 0.001
weight_decay: 0
)
Adam (
Parameter Group 0
amsgrad: False
betas: (0.5, 0.99)
eps: 1e-08
lr: 0.0001
weight_decay: 0
)
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