Pierrotlc's group workspace
Group: 64x64
Name
2 visualized
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
2h 58m 44s
-
64
3
./images/
-
64x64
-
-
128
20
5
-
-
-
-
0.01
256
0.0005
200
42
-
-
0.50146
0.52646
0.50672
0.49674
0.52588
0.502
Failed
pierrotlc
1h 3m 5s
-
64
3
./images/
-
64x64
-
-
400
12
6
-
-
-
-
0.01
512
0.0005
200
42
-
-
0.52359
0.22265
0.52581
0.52113
0.22326
0.52336
Finished
pierrotlc
1h 16m 42s
-
64
3
./images/
-
64x64
-
-
512
12
6
-
-
-
-
0.01
256
0.0005
100
42
-
-
0.51817
0.22269
0.5204
0.51439
0.22295
0.51662
Finished
pierrotlc
1h 13m 55s
-
64
3
./images/
-
64x64
-
-
256
8
6
-
-
-
-
0.01
256
0.0005
100
42
-
-
0.52255
0.3209
0.52576
0.51989
0.32053
0.5231
Failed
pierrotlc
22m 55s
-
64
3
./images/
-
64x64
-
-
256
8
6
-
-
-
-
0.05
128
0.0005
100
42
-
-
0.54198
0.10413
0.54719
0.54117
0.10442
0.54639
Failed
pierrotlc
25m 50s
-
64
3
./images/
-
64x64
-
-
256
8
6
-
-
-
-
0.1
256
0.0001
50
42
-
-
0.54839
0.075977
0.55598
0.54737
0.075611
0.55493
Crashed
pierrotlc
43m 52s
-
64
3
./images/
-
64x64
-
-
256
8
5
-
-
-
-
0.1
128
0.001
100
42
-
-
0.51604
0.093205
0.52536
0.51513
0.093212
0.52446
Failed
pierrotlc
55m 22s
-
64
3
./images/
-
64x64
-
-
92
8
5
-
-
-
-
0.1
128
0.001
100
42
-
-
0.52551
0.12504
0.53801
0.52437
0.12528
0.5369
Finished
pierrotlc
39m 55s
-
64
3
./images/
-
64x64
-
-
92
8
5
-
-
-
-
0.1
128
0.0001
50
42
-
-
0.53027
0.11172
0.54144
0.52967
0.11177
0.54085
Finished
pierrotlc
36m 26s
-
64
3
./images/
cuda
64x64
[128,3,64,64]
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 0x7fc226f434c0>
<src.dataset.AnimeDataset object at 0x7fc226f43250>
0.1
128
0.001
50
42
<torch.utils.data.dataloader.DataLoader object at 0x7fc226f42fb0>
<src.dataset.AnimeDataset object at 0x7fc226f430a0>
0.51077
0.12616
0.52339
0.51025
0.12608
0.52286
Failed
pierrotlc
19m 15s
-
64
3
./images/
cuda
64x64
[128,3,64,64]
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 0x7fddb39833a0>
<src.dataset.AnimeDataset object at 0x7fddb3983130>
0.01
128
0.001
50
42
<torch.utils.data.dataloader.DataLoader object at 0x7fddb3982e90>
<src.dataset.AnimeDataset object at 0x7fddb3982f80>
0.49457
0.56949
0.50026
0.49432
0.56871
0.50001
1-11
of 11