pierrotlc

Pierrotlc's group workspace

Group: Wasserstein - 32x32

1-4

of 4

Tags

Notes

Author

pierrotlc

State

Failed

Start time

January 24th, 2022 4:43:40 PM

Runtime

27m 20s

Tracked hours

27m 16s

Run path

pierrotlc/AnimeStyleGAN/9mfxwrfc

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 "misty-dew-72" ae8f98769756d947b272a72179819c7bbc94ce0c

Command

launch_training.py

System Hardware

CPU count	16
GPU count	1
GPU type	NVIDIA GeForce RTX 3080 Laptop GPU

W&B CLI Version

0.12.9

Group

Wasserstein - 32x32

Config parameters are your model's inputs. Learn more

▶
Config parameters:{} 23 keys
- batch_size:
  128
- ▶
  betas_d:[] 2 items
  - 0:
    0.5
  - 1:
    0.99
- ▶
  betas_g:[] 2 items
  - 0:
    0.7
  - 1:
    0.99
- device:
  "cuda"
- dim_image:
  32
- dim_z:
  32
- dropout:
  0.3
- epochs:
  100
- iter_D:
  3
- lr_d:
  0.00001
- lr_g:
  0.00005
- n_channels:
  128
- n_first_channels:
  8
- n_layers_d_block:
  2
- n_layers_z:
  3
- netD:
  "Discriminator( (first_conv): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(3, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (blocks): ModuleList( (0): DiscriminatorBlock( (convs): ModuleList( (0): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) (1): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) ) (downsample): Conv2d(8, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) (1): DiscriminatorBlock( (convs): ModuleList( (0): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) (1): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) ) (downsample): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) (2): DiscriminatorBlock( (convs): ModuleList( (0): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) (1): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) ) (downsample): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) (3): DiscriminatorBlock( (convs): ModuleList( (0): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) (1): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) ) (downsample): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) (4): DiscriminatorBlock( (convs): ModuleList( (0): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) (1): Sequential( (0): Dropout(p=0.3, inplace=False) (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): LeakyReLU(negative_slope=0.01) ) ) (downsample): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) ) (classify): Sequential( (0): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): Flatten(start_dim=1, end_dim=-1) ) )"
- netG:
  "StyleGAN( (mapping): MappingNetwork( (norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True) (fully_connected): 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) ) ) (out_layer): Linear(in_features=32, out_features=32, bias=True) ) (synthesis): SynthesisNetwork( (blocks): ModuleList( (0): SynthesisBlock( (conv2): Sequential( (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.01) ) (ada_in): AdaIN() ) (1): SynthesisBlock( (upsample): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (conv1): Sequential( (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.01) ) (conv2): Sequential( (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.01) ) (ada_in): AdaIN() ) (2): SynthesisBlock( (upsample): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (conv1): Sequential( (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.01) ) (conv2): Sequential( (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.01) ) (ada_in): AdaIN() ) (3): SynthesisBlock( (upsample): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (conv1): Sequential( (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.01) ) (conv2): Sequential( (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.01) ) (ada_in): AdaIN() ) ) (to_rgb): Conv2d(16, 3, kernel_size=(1, 1), stride=(1, 1)) ) )"
- optimD:
  "Adam ( Parameter Group 0 amsgrad: False betas: (0.5, 0.99) eps: 1e-08 lr: 1e-05 weight_decay: 0 )"
- optimG:
  "Adam ( Parameter Group 0 amsgrad: False betas: (0.7, 0.99) eps: 1e-08 lr: 5e-05 weight_decay: 0 )"
- seed:
  0
- test_loader:
  "<torch.utils.data.dataloader.DataLoader object at 0x7f65d24dac10>"
- train_loader:
  "<torch.utils.data.dataloader.DataLoader object at 0x7f65d24dab20>"
- weight_GP:
  3

Summary metrics are your model's outputs. Learn more

▶
Summary metrics:{} 15 keys
- ▶
  Generated images:{} 7 keys
- Test - D_fake_acc:
  0
- Test - D_fake_loss:
  287.48831894818477
- Test - D_GP:
  938.692138671875
- Test - D_real_acc:
  1
- Test - D_real_loss:
  -340.5576961741728
- Test - D_total_acc:
  0.5
- Test - D_total_loss:
  2,763.007008272059
- Train - D_fake_acc:
  0
- Train - D_fake_loss:
  287.53100405241315
- Train - D_GP:
  931.265918932463
- Train - D_real_acc:
  1
- Train - D_real_loss:
  -339.219056581196
- Train - D_total_acc:
  0.5
- Train - D_total_loss:
  2,742.10970105623

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

wandb-history

run-9mfxwrfc-history:v0