Skip to main content
pierrotlc
Projects
AnimeStyleGAN
Run groups
Running average - 32x32
Group
Workspace
Runs
Logs
Files
Artifacts

Pierrotlc's group workspace

Running average - 32x32

What makes this group special?
Tags

easy-plant-168

Notes
Author
pierrotlc
State
Failed
Start time
January 31st, 2022 2:38:26 PM
Runtime
1h 38s
Tracked hours
1h 33s
Run path
pierrotlc/AnimeStyleGAN/16hv2i2f
OS
Linux-5.15.15-76051515-generic-x86_64-with-glibc2.10
Python version
3.8.5
Git repository
git clone git@github.com:Futurne/AnimeStyleGAN.git
Git state
git checkout -b "easy-plant-168" 0e75ad077ee112f218d1481d60fa292fb1b940be
Command
launch_training.py
System Hardware
CPU count16
GPU count1
GPU typeNVIDIA GeForce RTX 3080 Laptop GPU
W&B CLI Version
0.12.9
Group
Running average - 32x32
Config

Config parameters are your model's inputs. Learn more

  • ▶
    {} 35 keys
    • 32
    • ▶
      [] 2 items
      • 0.5
      • 0.99
    • ▶
      [] 2 items
      • 0.5
      • 0.5
    • "<torch.utils.data.dataloader.DataLoader object at 0x7f476d68d1c0>"
    • "cuda"
    • 32
    • 32
    • 0.3
    • 50
    • 0.65
    • 0.5
    • 0.001
    • 0.001
    • ▶
      [] 6 items
      • ▶
        [] 6 items
        • 256
        • 12
        • 1
        • 50
        • 2
        • 4
        • "Discriminator( (first_conv): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(3, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (blocks): ModuleList( (0): DiscriminatorBlock( (convs): ModuleList( (0): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) (1): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(12, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) ) (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)) ) ) (classify): Sequential( (0): Conv2d(384, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): Flatten(start_dim=1, end_dim=-1) ) )"
        • "StyleGAN( (mapping): MappingNetwork( (norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True) (layers): ModuleList( (0): Sequential( (0): Linear(in_features=32, out_features=32, bias=True) (1): LayerNorm((32,), eps=1e-05, elementwise_affine=True) (2): LeakyReLU(negative_slope=0.01) ) (1): Sequential( (0): Linear(in_features=32, out_features=32, bias=True) (1): LayerNorm((32,), eps=1e-05, elementwise_affine=True) (2): LeakyReLU(negative_slope=0.01) ) (2): Sequential( (0): Linear(in_features=32, out_features=32, bias=True) (1): LayerNorm((32,), eps=1e-05, elementwise_affine=True) (2): LeakyReLU(negative_slope=0.01) ) (3): Sequential( (0): Linear(in_features=32, out_features=32, bias=True) (1): LayerNorm((32,), eps=1e-05, elementwise_affine=True) (2): LeakyReLU(negative_slope=0.01) ) ) (out): Linear(in_features=32, out_features=32, bias=True) ) (synthesis): SynthesisNetwork( (blocks): ModuleList( (0): SynthesisBlock( (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() ) (1): 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() ) (2): 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() ) (3): 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() ) (4): 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 0x7f477435b910>"
        • "<torch.optim.lr_scheduler.MultiStepLR object at 0x7f476d663e80>"
        • 5
        • 5
        • 0
        • 0
        • 1
      Summary

      Summary metrics are your model's outputs. Learn more

      • ▶
        {} 10 keys
        • 0.4796371817588806
        • 0.7032634341716766
        • 0.4570188874006271
        • 0.7403833258152008
        • 1.7948978918790817
        • 2.8136830163002013
        • ▶
          {} 7 keys
          • 0.7465790092945099
          • 0.04698708038777113
          • 0.2037570294737816
        Artifact Outputs

        This run produced these artifacts as outputs. Total: 1. Learn more

        1-1
        of 1
        Type
        Name
        Consumer count
        wandb-history
        run-16hv2i2f-history:v0
        0
        Loading...