Pierrotlc's group workspace
Group: 32x32
State
Notes
User
Tags
Created
Runtime
Sweep
data.image_size
data.n_channels
data.path_dir
device
group
input_size
model
net_arch.n_channels_latent
net_arch.n_filters
net_arch.n_layers
optimizer
prepared
test_loader
test_set
train.KLD_weight
train.batch_size
train.lr
train.n_epochs
train.seed
train_loader
train_set
Test - BCE
Test - KLD
Test - loss
Train - BCE
Train - KLD
Train - loss
Finished
-
pierrotlc
1h 26m 10s
-
32
3
./images/
-
32x32
-
-
256
8
5
-
-
-
-
0.001
256
0.001
100
42
-
-
0.52205
0.82589
0.52287
0.521
0.8247
0.52183
Finished
-
pierrotlc
1h 23m 54s
-
32
3
./images/
-
32x32
-
-
256
8
5
-
-
-
-
0.01
256
0.001
100
42
-
-
0.52771
0.3095
0.5308
0.52635
0.30884
0.52944
Finished
-
pierrotlc
8m 29s
-
32
3
./images/
-
32x32
-
-
256
8
5
-
-
-
-
0.01
256
0.001
10
42
-
-
0.54813
0.15501
0.54968
0.5471
0.15545
0.54866
Finished
-
pierrotlc
28m 38s
-
32
3
./images/
cuda
32x32
[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)
)
)
64
16
4
Adam (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
capturable: False
eps: 1e-08
foreach: None
lr: 0.001
maximize: False
weight_decay: 0
)
true
<torch.utils.data.dataloader.DataLoader object at 0x7f4b445773a0>
<src.dataset.AnimeDataset object at 0x7f4b44577130>
0.01
128
0.001
50
42
<torch.utils.data.dataloader.DataLoader object at 0x7f4b44576e90>
<src.dataset.AnimeDataset object at 0x7f4b44576f80>
0.51702
0.57045
0.52272
0.5165
0.57015
0.5222
Failed
-
pierrotlc
10m 18s
-
32
3
./images/
cuda
32x32
[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)
)
)
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 0x7f743ac3f3a0>
<src.dataset.AnimeDataset object at 0x7f743ac3f130>
2
128
0.0001
50
42
<torch.utils.data.dataloader.DataLoader object at 0x7f743ac3ee90>
<src.dataset.AnimeDataset object at 0x7f743ac3ef80>
0.59132
0.0058031
0.60293
0.59144
0.0058311
0.6031
Failed
-
pierrotlc
9m 1s
-
32
3
./images/
cuda
32x32
[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)
)
)
64
16
4
Adam (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
capturable: False
eps: 1e-08
foreach: None
lr: 0.001
maximize: False
weight_decay: 0
)
true
<torch.utils.data.dataloader.DataLoader object at 0x7f509ea733a0>
<src.dataset.AnimeDataset object at 0x7f509ea73130>
2
128
0.001
50
42
<torch.utils.data.dataloader.DataLoader object at 0x7f509ea72e90>
<src.dataset.AnimeDataset object at 0x7f509ea72f80>
0.58769
0.0074634
0.60262
0.5875
0.0074766
0.60245
Failed
-
pierrotlc
8m 40s
-
32
3
./images/
cuda
32x32
[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)
)
)
64
16
4
Adam (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
capturable: False
eps: 1e-08
foreach: None
lr: 0.001
maximize: False
weight_decay: 0
)
true
<torch.utils.data.dataloader.DataLoader object at 0x7fbf2a68b430>
<src.dataset.AnimeDataset object at 0x7fbf2a68b1c0>
2
128
0.001
50
42
<torch.utils.data.dataloader.DataLoader object at 0x7fbf2a68af20>
<src.dataset.AnimeDataset object at 0x7fbf2a68b010>
0.59121
0.005932
0.60307
0.59126
0.0059458
0.60315
1-7
of 7