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G_printed.txt
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G_printed.txt
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UnetGenerator (
(model): UnetSkipConnectionBlock (
(model): Sequential (
(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): UnetSkipConnectionBlock (
(model): Sequential (
(0): LeakyReLU (0.2, inplace)
(1): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
(3): UnetSkipConnectionBlock (
(model): Sequential (
(0): LeakyReLU (0.2, inplace)
(1): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
(3): UnetSkipConnectionBlock (
(model): Sequential (
(0): LeakyReLU (0.2, inplace)
(1): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
(3): UnetSkipConnectionBlock (
(model): Sequential (
(0): LeakyReLU (0.2, inplace)
(1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
(3): UnetSkipConnectionBlock (
(model): Sequential (
(0): LeakyReLU (0.2, inplace)
(1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
(3): UnetSkipConnectionBlock (
(model): Sequential (
(0): LeakyReLU (0.2, inplace)
(1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
(3): UnetSkipConnectionBlock (
(model): Sequential (
(0): LeakyReLU (0.2, inplace)
(1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(2): ReLU (inplace)
(3): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
)
)
(4): ReLU (inplace)
(5): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
(7): Dropout (p = 0.5)
)
)
(4): ReLU (inplace)
(5): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
(7): Dropout (p = 0.5)
)
)
(4): ReLU (inplace)
(5): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
(7): Dropout (p = 0.5)
)
)
(4): ReLU (inplace)
(5): ConvTranspose2d(1024, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
)
)
(4): ReLU (inplace)
(5): ConvTranspose2d(512, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
)
)
(4): ReLU (inplace)
(5): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
)
)
(2): ReLU (inplace)
(3): ConvTranspose2d(128, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(4): Tanh ()
)
)
)
Total number of parameters: 54413955