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512

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2

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"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) ) ) (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) ) ) (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) ) ) (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) ) ) (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) ) ) (downsample): Conv2d(192, 384, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) (5): DiscriminatorBlock( (convs): ModuleList( (0): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(384, 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(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) ) (downsample): Conv2d(384, 768, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) ) (classify): Sequential( (0): Conv2d(768, 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(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): LeakyReLU(negative_slope=0.01) ) (ada_in): AdaIN() ) (1): SynthesisBlock( (upsample): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (conv1): 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) ) (conv2): 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() ) (2): SynthesisBlock( (upsample): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (conv1): 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) ) (conv2): 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() ) (3): SynthesisBlock( (upsample): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (conv1): 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) ) (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() ) (4): 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() ) (5): 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() ) ) (to_rgb): Conv2d(16, 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 )"

0.99

0.99

0

"<torch.optim.lr_scheduler.MultiStepLR object at 0x7f8c5f9f8910>"

"<torch.optim.lr_scheduler.MultiStepLR object at 0x7f8c5806b550>"

5

5

0

0

1

0.6087873388230801

0.9838115244507788

0.5968591649532318

0.6107392708659172

1.1825268886834384

3.1082159925699235

0.6034856987595558

0.07619765437021851

0.3851378194838762