ubuntu@ip-Address:~/DeblurGAN$ python test.py --dataroot mydata --model experiment_name --dataset_mode single --learn_residual
------------ Options -------------
aspect_ratio: 1.0
batchSize: 1
checkpoints_dir: ./checkpoints
dataroot: mydata
dataset_mode: single
display_id: 1
display_port: 8097
display_single_pane_ncols: 0
display_winsize: 256
fineSize: 256
gan_type: wgan-gp
gpu_ids: [0]
how_many: 5000
input_nc: 3
isTrain: False
learn_residual: True
loadSizeX: 640
loadSizeY: 360
max_dataset_size: inf
model: experiment_name
nThreads: 2
n_layers_D: 3
name: experiment_name
ndf: 64
ngf: 64
no_dropout: False
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 [SingleImageDataset] was created
/usr/local/lib/python2.7/dist-packages/torchvision/transforms/transforms.py:156: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
"please use transforms.Resize instead.")
---------- 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): Dropout(p=0.5)
(5): ReflectionPad2d((1, 1, 1, 1))
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
(7): 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): Dropout(p=0.5)
(5): ReflectionPad2d((1, 1, 1, 1))
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
(7): 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): Dropout(p=0.5)
(5): ReflectionPad2d((1, 1, 1, 1))
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
(7): 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): Dropout(p=0.5)
(5): ReflectionPad2d((1, 1, 1, 1))
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
(7): 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): Dropout(p=0.5)
(5): ReflectionPad2d((1, 1, 1, 1))
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
(7): 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): Dropout(p=0.5)
(5): ReflectionPad2d((1, 1, 1, 1))
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
(7): 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): Dropout(p=0.5)
(5): ReflectionPad2d((1, 1, 1, 1))
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
(7): 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): Dropout(p=0.5)
(5): ReflectionPad2d((1, 1, 1, 1))
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
(7): 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): Dropout(p=0.5)
(5): ReflectionPad2d((1, 1, 1, 1))
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
(7): 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 [ConditionalGANModel] was created
Traceback (most recent call last):
File "test.py", line 35, in <module>
model.set_input(data)
File "/home/ubuntu/DeblurGAN/models/conditional_gan_model.py", line 69, in set_input
input_B = input['B' if AtoB else 'A']
KeyError: 'B'
ubuntu@ip-Address:~/DeblurGAN$ ^C
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