Pierrotlc's workspace
Runs
25
Name
18 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
Crashed
pierrotlc
3d 10h 46m 24s
-
64
3
./images/
cuda
64x64
64.75
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)
)
)
216
11.27273
5.27273
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>","<torch.utils.data.dataloader.DataLoader object at 0x7fddb39833a0>"]
["<src.dataset.AnimeDataset object at 0x7fc226f43250>","<src.dataset.AnimeDataset object at 0x7fddb3983130>"]
0.054545
209.45455
0.00060909
100
42
["<torch.utils.data.dataloader.DataLoader object at 0x7fc226f42fb0>","<torch.utils.data.dataloader.DataLoader object at 0x7fddb3982e90>"]
["<src.dataset.AnimeDataset object at 0x7fc226f430a0>","<src.dataset.AnimeDataset object at 0x7fddb3982f80>"]
0.52121
0.22713
0.52821
0.51949
0.22706
0.5265
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
2d 1h 32m 34s
-
32
3
./images/
cuda
32x32
48.75
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)
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(2): LeakyReLU(negative_slope=0.01)
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(2): LeakyReLU(negative_slope=0.01)
)
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)
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)
(project_rgb): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1), padding=same)
)
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Failed
pierrotlc
7h 35m 2s
-
32
3
./images/
cuda
-
44.17857
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Sequential(\n (0): ResBlock(\n (conv_block): Sequential(\n (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n )\n (4): Sequential(\n (0): ResBlock(\n (conv_block): Sequential(\n (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(512, 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LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n )\n (3): Sequential(\n (0): ResBlock(\n (conv_block): Sequential(\n (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n )\n (4): Sequential(\n (0): ResBlock(\n (conv_block): Sequential(\n (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), 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ResBlock(\n (conv_block): Sequential(\n (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n )\n (3): Sequential(\n (0): ResBlock(\n (conv_block): Sequential(\n (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, 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momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n )\n (2): Sequential(\n (0): ResBlock(\n (conv_block): Sequential(\n (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n )\n (3): Sequential(\n (0): ResBlock(\n (conv_block): Sequential(\n (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n )\n )\n )\n (project_latent): Sequential(\n (0): Conv2d(128, 48, kernel_size=(3, 3), stride=(1, 1), padding=same)\n (1): Rearrange('b (d e) w h -> b d e w h', d=2)\n )\n )\n (decoder): VAEDecoder(\n (cnn_decoder): CNNDecoder(\n (project_layer): Sequential(\n (0): Conv2d(24, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n (layers): ModuleList(\n (0): Sequential(\n (0): ResBlock(\n (conv_block): Sequential(\n (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n )\n (1): Sequential(\n (0): ResBlock(\n (conv_block): Sequential(\n (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n )\n (2): Sequential(\n (0): ResBlock(\n (conv_block): Sequential(\n (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n )\n (3): Sequential(\n (0): ResBlock(\n (conv_block): Sequential(\n (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)\n (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n (1): ReduceBlock(\n (conv_block): Sequential(\n (0): ConvTranspose2d(16, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.01)\n )\n )\n )\n )\n )\n (project_rgb): Conv2d(8, 3, kernel_size=(3, 3), stride=(1, 1), padding=same)\n )\n)"]
58.28571
14.85714
4.28571
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 0x7efe1d07a5c0>","<torch.utils.data.dataloader.DataLoader object at 0x7f6b3324b700>","<torch.utils.data.dataloader.DataLoader object at 0x7f989f73b190>","<torch.utils.data.dataloader.DataLoader object at 0x7f9c1a886650>","<torch.utils.data.dataloader.DataLoader object at 0x7faff2c830a0>","<torch.utils.data.dataloader.DataLoader object at 0x7fd8dea836a0>","<torch.utils.data.dataloader.DataLoader object at 0x7fd9ac28b130>"]
["<src.dataset.AnimeDataset object at 0x7efe1d07b130>","<src.dataset.AnimeDataset object at 0x7f6b3324b6a0>","<src.dataset.AnimeDataset object at 0x7f989f73b250>","<src.dataset.AnimeDataset object at 0x7f9c1a8871c0>","<src.dataset.AnimeDataset object at 0x7faff2c83160>","<src.dataset.AnimeDataset object at 0x7fd8dea83640>","<src.dataset.AnimeDataset object at 0x7fd9ac28b1f0>"]
1.64286
109.71429
0.0001
10
42
["<torch.utils.data.dataloader.DataLoader object at 0x7efe1d07ac50>","<torch.utils.data.dataloader.DataLoader object at 0x7f6b3324b3a0>","<torch.utils.data.dataloader.DataLoader object at 0x7f989f73add0>","<torch.utils.data.dataloader.DataLoader object at 0x7f9c1a886ce0>","<torch.utils.data.dataloader.DataLoader object at 0x7faff2c82ce0>","<torch.utils.data.dataloader.DataLoader object at 0x7fd8dea83340>","<torch.utils.data.dataloader.DataLoader object at 0x7fd9ac28ad70>"]
["<src.dataset.AnimeDataset object at 0x7efe1d07af20>","<src.dataset.AnimeDataset object at 0x7f6b3324b550>","<src.dataset.AnimeDataset object at 0x7f989f73b010>","<src.dataset.AnimeDataset object at 0x7f9c1a886fb0>","<src.dataset.AnimeDataset object at 0x7faff2c82f20>","<src.dataset.AnimeDataset object at 0x7fd8dea834f0>","<src.dataset.AnimeDataset object at 0x7fd9ac28afb0>"]
-29.76056
0.26638
-29.47769
-30.10295
0.26746
-29.81888
Finished
pierrotlc
2m 47s
-
32
3
./images/
cuda
-
[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 0x7f9c1a886650>
<src.dataset.AnimeDataset object at 0x7f9c1a8871c0>
2
128
0.0001
5
42
<torch.utils.data.dataloader.DataLoader object at 0x7f9c1a886ce0>
<src.dataset.AnimeDataset object at 0x7f9c1a886fb0>
0.59421
0.0053669
0.60495
0.59407
0.0053744
0.60482
Failed
pierrotlc
2m 7s
-
32
3
./images/
cuda
-
[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 0x7efe1d07a5c0>
<src.dataset.AnimeDataset object at 0x7efe1d07b130>
2
128
0.0001
5
42
<torch.utils.data.dataloader.DataLoader object at 0x7efe1d07ac50>
<src.dataset.AnimeDataset object at 0x7efe1d07af20>
-8.90084
0.0070125
-8.88681
-8.89789
0.0072638
-8.88336
Finished
pierrotlc
2m 57s
-
32
3
./images/
cuda
-
[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 0x7fd9ac28b130>
<src.dataset.AnimeDataset object at 0x7fd9ac28b1f0>
2
128
0.0001
5
42
<torch.utils.data.dataloader.DataLoader object at 0x7fd9ac28ad70>
<src.dataset.AnimeDataset object at 0x7fd9ac28afb0>
-22.41522
0.1077
-22.19982
-22.47697
0.10778
-22.26141
Finished
pierrotlc
3m 7s
-
32
3
./images/
cuda
-
[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 0x7faff2c830a0>
<src.dataset.AnimeDataset object at 0x7faff2c83160>
2
128
0.0001
5
42
<torch.utils.data.dataloader.DataLoader object at 0x7faff2c82ce0>
<src.dataset.AnimeDataset object at 0x7faff2c82f20>
-5.95138
0.09975
-5.75188
-6.02728
0.1001
-5.82708
Failed
pierrotlc
8m 13s
-
32
3
./images/
cuda
-
[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)
)
)
)
(4): 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): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
)
)
)
(project_latent): Sequential(
(0): Conv2d(512, 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, 512, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)
(1): BatchNorm2d(512, 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(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)
(1): BatchNorm2d(512, 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(512, 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)
)
)
)
(1): 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)
)
)
)
(2): 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)
)
)
)
(3): 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)
)
)
)
(4): 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
5
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 0x7f6b3324b700>
<src.dataset.AnimeDataset object at 0x7f6b3324b6a0>
2
128
0.0001
20
42
<torch.utils.data.dataloader.DataLoader object at 0x7f6b3324b3a0>
<src.dataset.AnimeDataset object at 0x7f6b3324b550>
-21.99898
0.2236
-21.55178
-22.38432
0.22422
-21.93589
Failed
pierrotlc
14m 17s
-
32
3
./images/
cuda
-
[64,3,32,32]
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)
)
)
)
(4): 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): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
)
)
)
(project_latent): Sequential(
(0): Conv2d(512, 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, 512, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)
(1): BatchNorm2d(512, 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(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)
(1): BatchNorm2d(512, 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(512, 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)
)
)
)
(1): 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)
)
)
)
(2): 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)
)
)
)
(3): 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)
)
)
)
(4): 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
5
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 0x7fd8dea836a0>
<src.dataset.AnimeDataset object at 0x7fd8dea83640>
1
64
0.0001
20
42
<torch.utils.data.dataloader.DataLoader object at 0x7fd8dea83340>
<src.dataset.AnimeDataset object at 0x7fd8dea834f0>
-125.17242
0.76529
-124.40714
-126.89707
0.77062
-126.12646
Finished
pierrotlc
6m 22s
-
32
3
./images/
cuda
-
[64,3,32,32]
VAE(
(encoder): VAEEncoder(
(cnn_encoder): CNNEncoder(
(project_layer): Sequential(
(0): Conv2d(3, 8, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)
(1): BatchNorm2d(8, 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(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)
(1): BatchNorm2d(8, 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(8, 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)
)
)
)
(1): 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)
)
)
)
(2): 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)
)
)
)
(3): 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)
)
)
)
)
)
(project_latent): Sequential(
(0): Conv2d(128, 48, 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(24, 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)
)
(layers): ModuleList(
(0): 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)
)
)
)
(1): 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)
)
)
)
(2): 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)
)
)
)
(3): 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): ConvTranspose2d(16, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
)
)
)
(project_rgb): Conv2d(8, 3, kernel_size=(3, 3), stride=(1, 1), padding=same)
)
)
24
8
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 0x7f989f73b190>
<src.dataset.AnimeDataset object at 0x7f989f73b250>
0.5
64
0.0001
10
42
<torch.utils.data.dataloader.DataLoader object at 0x7f989f73add0>
<src.dataset.AnimeDataset object at 0x7f989f73b010>
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1-3
of 3