Building Blocks

class AutoTransConv[source]

AutoTransConv(n_in, n_out, ks, stride, bias=True) :: Module

Automatic padding of transpose convolution for input-output feature size

trans_conv_norm_relu[source]

trans_conv_norm_relu(n_in, n_out, norm_layer, bias, ks=3, stride=2)

Transpose convolutional layer

pad_conv_norm_relu[source]

pad_conv_norm_relu(n_in, n_out, norm_layer, padding_mode='zeros', pad=1, ks=3, stride=1, activ=True, bias=True)

Adding ability to specify different paddings to convolutional layer

conv_norm_relu[source]

conv_norm_relu(n_in, n_out, norm_layer=None, ks=3, bias:bool=True, pad=1, stride=1, activ=True, a=0.2)

Convolutional layer

class ResBlock[source]

ResBlock(dim, padding_mode, bias, dropout, norm_layer='InstanceNorm2d') :: Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

Generator

generator[source]

generator(n_in, n_out, n_f=64, norm_layer=None, dropout=0.0, n_blocks=6, pad_mode='reflection')

Generator that maps an input of one domain to the other

generator(3,10)

Sequential(
  (0): ReflectionPad2d((0, 0, 0, 0))
  (1): AutoConv(
    (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), padding_mode=reflection)
  )
  (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
  (3): ReLU(inplace=True)
  (4): AutoConv(
    (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
  )
  (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
  (6): ReLU(inplace=True)
  (7): AutoConv(
    (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
  )
  (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
  (9): ReLU(inplace=True)
  (10): ResBlock(
    (xb): Sequential(
      (0): ReflectionPad2d((0, 0, 0, 0))
      (1): AutoConv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
      )
      (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (3): ReLU(inplace=True)
      (4): ReflectionPad2d((0, 0, 0, 0))
      (5): AutoConv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
      )
      (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    )
  )
  (11): ResBlock(
    (xb): Sequential(
      (0): ReflectionPad2d((0, 0, 0, 0))
      (1): AutoConv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
      )
      (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (3): ReLU(inplace=True)
      (4): ReflectionPad2d((0, 0, 0, 0))
      (5): AutoConv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
      )
      (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    )
  )
  (12): ResBlock(
    (xb): Sequential(
      (0): ReflectionPad2d((0, 0, 0, 0))
      (1): AutoConv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
      )
      (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (3): ReLU(inplace=True)
      (4): ReflectionPad2d((0, 0, 0, 0))
      (5): AutoConv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
      )
      (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    )
  )
  (13): ResBlock(
    (xb): Sequential(
      (0): ReflectionPad2d((0, 0, 0, 0))
      (1): AutoConv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
      )
      (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (3): ReLU(inplace=True)
      (4): ReflectionPad2d((0, 0, 0, 0))
      (5): AutoConv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
      )
      (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    )
  )
  (14): ResBlock(
    (xb): Sequential(
      (0): ReflectionPad2d((0, 0, 0, 0))
      (1): AutoConv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
      )
      (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (3): ReLU(inplace=True)
      (4): ReflectionPad2d((0, 0, 0, 0))
      (5): AutoConv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
      )
      (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    )
  )
  (15): ResBlock(
    (xb): Sequential(
      (0): ReflectionPad2d((0, 0, 0, 0))
      (1): AutoConv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
      )
      (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (3): ReLU(inplace=True)
      (4): ReflectionPad2d((0, 0, 0, 0))
      (5): AutoConv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
      )
      (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    )
  )
  (16): AutoTransConv(
    (conv): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
  )
  (17): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
  (18): ReLU()
  (19): AutoTransConv(
    (conv): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
  )
  (20): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
  (21): ReLU()
  (22): ReflectionPad2d((3, 3, 3, 3))
  (23): Conv2d(64, 10, kernel_size=(7, 7), stride=(1, 1))
  (24): Tanh()
)

Discriminator

discriminator[source]

discriminator(c_in, n_f, n_layers, norm_layer=None, sigmoid=False)

Discrminator to classify input as belonging to one class or the other

discriminator(3, 6, 3)

Sequential(
  (0): Conv2d(3, 6, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (1): LeakyReLU(negative_slope=0.2, inplace=True)
  (2): Conv2d(6, 12, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (3): InstanceNorm2d(12, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
  (4): LeakyReLU(negative_slope=0.2, inplace=True)
  (5): Conv2d(12, 24, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (6): InstanceNorm2d(24, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
  (7): LeakyReLU(negative_slope=0.2, inplace=True)
  (8): Conv2d(24, 48, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
  (9): InstanceNorm2d(48, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
  (10): LeakyReLU(negative_slope=0.2, inplace=True)
  (11): Conv2d(48, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
)

Loss and Trainer

class cycleGAN[source]

cycleGAN(c_in, c_out, n_f=64, disc_layers=3, gen_blocks=6, drop=0.0, norm_layer=None, sigmoid=False) :: Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

class DynamicLoss[source]

DynamicLoss(loss_fn) :: Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

class CycleGANLoss[source]

CycleGANLoss(model, loss_fn='mse_loss', la=10.0, lb=10, lid=0.5) :: Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

CycleGAN loss is composed of 3 parts:

1. Identity, an image that has gone through its own domain should remain the same
2. Generator, the output images should fool the discriminator into thinking they belong to that class
3. Cyclical loss, and image that has been mapped to the other domain then mapped back to itself should resemble the original input

class cycleGANTrainer[source]

cycleGANTrainer() :: Callback

Trainer to sequence timing of training both the discriminator as well as the generator's

Training

cgan = cycleGAN(3, 3, gen_blocks=9)
learn = get_learner(cgan, loss=CycleGANLoss(cgan))
run = get_runner(learn, [cycleGANTrainer()])

cgan

cycleGAN(
  (a_discriminator): Sequential(
    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (1): LeakyReLU(negative_slope=0.2, inplace=True)
    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (4): LeakyReLU(negative_slope=0.2, inplace=True)
    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (7): LeakyReLU(negative_slope=0.2, inplace=True)
    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
    (9): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (10): LeakyReLU(negative_slope=0.2, inplace=True)
    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
  )
  (b_discriminator): Sequential(
    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (1): LeakyReLU(negative_slope=0.2, inplace=True)
    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (4): LeakyReLU(negative_slope=0.2, inplace=True)
    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (7): LeakyReLU(negative_slope=0.2, inplace=True)
    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
    (9): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (10): LeakyReLU(negative_slope=0.2, inplace=True)
    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
  )
  (generate_a): Sequential(
    (0): ReflectionPad2d((0, 0, 0, 0))
    (1): AutoConv(
      (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), padding_mode=reflection)
    )
    (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (3): ReLU(inplace=True)
    (4): AutoConv(
      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    )
    (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (6): ReLU(inplace=True)
    (7): AutoConv(
      (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    )
    (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (9): ReLU(inplace=True)
    (10): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (11): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (12): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (13): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (14): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (15): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (16): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (17): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (18): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (19): AutoTransConv(
      (conv): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    )
    (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (21): ReLU()
    (22): AutoTransConv(
      (conv): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    )
    (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (24): ReLU()
    (25): ReflectionPad2d((3, 3, 3, 3))
    (26): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
    (27): Tanh()
  )
  (generate_b): Sequential(
    (0): ReflectionPad2d((0, 0, 0, 0))
    (1): AutoConv(
      (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), padding_mode=reflection)
    )
    (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (3): ReLU(inplace=True)
    (4): AutoConv(
      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    )
    (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (6): ReLU(inplace=True)
    (7): AutoConv(
      (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    )
    (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (9): ReLU(inplace=True)
    (10): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (11): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (12): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (13): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (14): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (15): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (16): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (17): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (18): ResBlock(
      (xb): Sequential(
        (0): ReflectionPad2d((0, 0, 0, 0))
        (1): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((0, 0, 0, 0))
        (5): AutoConv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode=reflection)
        )
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (19): AutoTransConv(
      (conv): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    )
    (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (21): ReLU()
    (22): AutoTransConv(
      (conv): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    )
    (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (24): ReLU()
    (25): ReflectionPad2d((3, 3, 3, 3))
    (26): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
    (27): Tanh()
  )
)