------------ Options -------------
aspect_ratio: 1.0
batchSize: 1
checkpoints_dir: ./checkpoints
dataroot: datasets/horse2zebra
dataset_mode: unaligned
display_id: 1
display_port: 8097
display_single_pane_ncols: 0
display_winsize: 256
fineSize: 256
gpu_ids: [0]
how_many: 50
identity: 0.0
input_nc: 3
isTrain: False
loadSize: 286
max_dataset_size: inf
model: cycle_gan
nThreads: 2
n_layers_D: 3
name: horse2zebra_cyclegan
ndf: 64
ngf: 64
no_dropout: True
no_flip: False
norm: instance
ntest: inf
output_nc: 3
phase: test
resize_or_crop: resize_and_crop
results_dir: ./results/
serial_batches: False
which_direction: AtoB
which_epoch: latest
which_model_netD: basic
which_model_netG: resnet_9blocks
-------------- End ----------------
CustomDatasetDataLoader
dataset [UnalignedDataset] was created
cycle_gan
---------- Networks initialized -------------
ResnetGenerator (
  (model): Sequential (
    (0): ReflectionPad2d (3, 3, 3, 3)
    (1): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1))
    (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False)
    (3): ReLU (inplace)
    (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
    (6): ReLU (inplace)
    (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
    (9): ReLU (inplace)
    (10): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (11): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (12): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (13): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (14): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (15): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (16): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (17): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (18): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (19): 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)
    (21): ReLU (inplace)
    (22): 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)
    (24): ReLU (inplace)
    (25): ReflectionPad2d (3, 3, 3, 3)
    (26): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
    (27): Tanh ()
  )
)
Total number of parameters: 11378179
ResnetGenerator (
  (model): Sequential (
    (0): ReflectionPad2d (3, 3, 3, 3)
    (1): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1))
    (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False)
    (3): ReLU (inplace)
    (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
    (6): ReLU (inplace)
    (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
    (9): ReLU (inplace)
    (10): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (11): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (12): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (13): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (14): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (15): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (16): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (17): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (18): ResnetBlock (
      (conv_block): Sequential (
        (0): ReflectionPad2d (1, 1, 1, 1)
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU (inplace)
        (4): ReflectionPad2d (1, 1, 1, 1)
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (19): 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)
    (21): ReLU (inplace)
    (22): 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)
    (24): ReLU (inplace)
    (25): ReflectionPad2d (3, 3, 3, 3)
    (26): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
    (27): Tanh ()
  )
)
Total number of parameters: 11378179
-----------------------------------------------
model [CycleGANModel] was created
process image... ['datasets/horse2zebra/testA/n02381460_1000.jpg']