UnetGenerator (
(model): UnetSkipConnectionBlock (
(model): Sequential (
(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(1): UnetSkipConnectionBlock (
(model): Sequential (
(0): LeakyReLU (0.2, inplace)
(1): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): InstanceNorm2d(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))
(2): InstanceNorm2d(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))
(2): InstanceNorm2d(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))
(2): InstanceNorm2d(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))
(2): InstanceNorm2d(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))
(2): InstanceNorm2d(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))
(2): ReLU (inplace)
(3): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(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))
(6): InstanceNorm2d(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))
(6): InstanceNorm2d(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))
(6): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
)
)
(4): ReLU (inplace)
(5): ConvTranspose2d(1024, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(6): InstanceNorm2d(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))
(6): InstanceNorm2d(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))
(6): InstanceNorm2d(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 ()
)
)
)
For example:
- How to get the module
Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
- How to get the module
(5): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
I want to change the attributes of certain layer in training in every iteration. Thank you in advance.