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animevae
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Test

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Author
pierrotlc
State
Finished
Start time
October 1st, 2022 8:26:38 AM
Runtime
2m 47s
Tracked hours
2m 42s
Run path
pierrotlc/animevae/3w0f6uu9
OS
Linux-5.19.11-200.fc36.x86_64-x86_64-with-glibc2.35
Python version
3.10.7
Git repository
git clone git@github.com:Futurne/anime_vae.git
Git state
git checkout -b "dulcet-shape-7" 44c5c84f7b5f9eb9f3ff43d7b6154fb480ab028c
Command
/home/pierrotlc/Documents/anime_vae/main.py default.yaml
System Hardware
CPU count16
GPU count1
GPU typeNVIDIA GeForce RTX 3080 Laptop GPU
W&B CLI Version
0.13.3
Group
Test
Config

Config parameters are your model's inputs. Learn more

  • ▶
    {} 12 keys
    • ▶
      {} 3 keys
      • 32
      • 3
      • "./images/"
    • "cuda"
    • ▶
      [] 4 items
      • 128
      • 3
      • 32
      • 32
    • "VAE( (encoder): VAEEncoder( (cnn_encoder): CNNEncoder( (project_layer): Sequential( (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False) (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) (layers): ModuleList( (0): Sequential( (0): ResBlock( (conv_block): Sequential( (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False) (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) (1): ReduceBlock( (conv_block): Sequential( (0): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) ) (1): Sequential( (0): ResBlock( (conv_block): Sequential( (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) (1): ReduceBlock( (conv_block): Sequential( (0): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) ) (2): Sequential( (0): ResBlock( (conv_block): Sequential( (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) (1): ReduceBlock( (conv_block): Sequential( (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) ) (3): Sequential( (0): ResBlock( (conv_block): Sequential( (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) (1): ReduceBlock( (conv_block): Sequential( (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) ) ) ) (project_latent): Sequential( (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=same) (1): Rearrange('b (d e) w h -> b d e w h', d=2) ) ) (decoder): VAEDecoder( (cnn_decoder): CNNDecoder( (project_layer): Sequential( (0): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) (layers): ModuleList( (0): Sequential( (0): ResBlock( (conv_block): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) (1): ReduceBlock( (conv_block): Sequential( (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) ) (1): Sequential( (0): ResBlock( (conv_block): Sequential( (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) (1): ReduceBlock( (conv_block): Sequential( (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) ) (2): Sequential( (0): ResBlock( (conv_block): Sequential( (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) (1): ReduceBlock( (conv_block): Sequential( (0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) ) (3): Sequential( (0): ResBlock( (conv_block): Sequential( (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) (1): ReduceBlock( (conv_block): Sequential( (0): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.01) ) ) ) ) ) (project_rgb): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1), padding=same) ) )"
    • ▶
      {} 3 keys
      • 64
      • 16
      • 4
    • "Adam ( Parameter Group 0 amsgrad: False betas: (0.9, 0.999) capturable: False eps: 1e-08 foreach: None lr: 0.0001 maximize: False weight_decay: 0 )"
    • true
    • "<torch.utils.data.dataloader.DataLoader object at 0x7f9c1a886650>"
    • "<src.dataset.AnimeDataset object at 0x7f9c1a8871c0>"
    • ▶
      {} 5 keys
      • "<torch.utils.data.dataloader.DataLoader object at 0x7f9c1a886ce0>"
      • "<src.dataset.AnimeDataset object at 0x7f9c1a886fb0>"
    Summary

    Summary metrics are your model's outputs. Learn more

    • ▶
      {} 9 keys
      • ▶
        {} 7 keys
        • 0.5942114138603211
        • ▶
          {} 7 keys
          • 0.005366912288591266
          • 0.604945238828659
          • 0.59407139588241
          • ▶
            {} 7 keys
            • 0.0053744194748338745
            • 0.6048202369380836
          Artifact Outputs

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

          1-1
          of 1
          Type
          Name
          Consumer count
          wandb-history
          run-3w0f6uu9-history:v0
          0
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