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jS5t3r/wide_resnet_2810.txt

Created February 24, 2022 08:54
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wide_resnet_2810.txt
DataParallel(
(module): Wide_ResNet(
(conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(layer1): Sequential(
(0): wide_basic(
(bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1): Conv2d(16, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dropout): Dropout(p=0.3, inplace=False)
(bn2): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(shortcut): Sequential(
(0): Conv2d(16, 160, kernel_size=(1, 1), stride=(1, 1))
)
)
(1): wide_basic(
(bn1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dropout): Dropout(p=0.3, inplace=False)
(bn2): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(shortcut): Sequential()
)
(2): wide_basic(
(bn1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dropout): Dropout(p=0.3, inplace=False)
(bn2): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(shortcut): Sequential()
)
(3): wide_basic(
(bn1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dropout): Dropout(p=0.3, inplace=False)
(bn2): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(shortcut): Sequential()
)
)
(layer2): Sequential(
(0): wide_basic(
(bn1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dropout): Dropout(p=0.3, inplace=False)
(bn2): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(shortcut): Sequential(
(0): Conv2d(160, 320, kernel_size=(1, 1), stride=(2, 2))
)
)
(1): wide_basic(
(bn1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dropout): Dropout(p=0.3, inplace=False)
(bn2): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(shortcut): Sequential()
)
(2): wide_basic(
(bn1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dropout): Dropout(p=0.3, inplace=False)
(bn2): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(shortcut): Sequential()
)
(3): wide_basic(
(bn1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dropout): Dropout(p=0.3, inplace=False)
(bn2): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(shortcut): Sequential()
)
)
(layer3): Sequential(
(0): wide_basic(
(bn1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1): Conv2d(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dropout): Dropout(p=0.3, inplace=False)
(bn2): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(shortcut): Sequential(
(0): Conv2d(320, 640, kernel_size=(1, 1), stride=(2, 2))
)
)
(1): wide_basic(
(bn1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dropout): Dropout(p=0.3, inplace=False)
(bn2): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(shortcut): Sequential()
)
(2): wide_basic(
(bn1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dropout): Dropout(p=0.3, inplace=False)
(bn2): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(shortcut): Sequential()
)
(3): wide_basic(
(bn1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dropout): Dropout(p=0.3, inplace=False)
(bn2): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(shortcut): Sequential()
)
)
(bn1): BatchNorm2d(640, eps=1e-05, momentum=0.9, affine=True, track_running_stats=True)
(linear): Linear(in_features=640, out_features=10, bias=True)
)
)
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