记录

转载http://blog.sina.com.cn/s/blog_a99f842a0102xfb3.html

------------ Options -------------

#from options.train_options import TrainOptions#opt = TrainOptions().parse()

batchSize: 1

beta1: 0.5

checkpoints_dir: ./checkpoints

continue_train: False

dataroot: ./datasets/girls

dataset_mode: unaligned

display_freq: 100

display_id: 1

display_port: 8097

display_single_pane_ncols: 0

display_winsize: 256

epoch_count: 1

fineSize: 256

gpu_ids: [0]

identity: 0.5

init_type: normal

input_nc: 3

isTrain: True

lambda_A: 10.0

lambda_B: 10.0

loadSize: 286

lr: 0.0002

lr_decay_iters: 50

lr_policy: lambda

max_dataset_size: inf

model: cycle_gan

nThreads: 2

n_layers_D: 3

name: girls_cyclegan

ndf: 64

ngf: 64

niter: 100

niter_decay: 100

no_dropout: True

no_flip: False

no_html: False

no_lsgan: False

norm: instance

output_nc: 3

phase: train

pool_size: 50

print_freq: 100

resize_or_crop: resize_and_crop

save_epoch_freq: 5

save_latest_freq: 5000

serial_batches: False

update_html_freq: 1000

which_direction: AtoB

which_epoch: latest

which_model_netD: basic

which_model_netG: resnet_9blocks

-------------- End ----------------

Custom Dataset Data Loader #print(data_loader.name())

dataset [UnalignedDataset] was created #self.dataset = CreateDataset(opt)

/anaconda/envs/python3.5/lib/python3.5/site-packages/torchvision-0.2.0-py3.5.egg/torchvision/transforms/transforms.py:156: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.

#training images = 2062   #print('#training images = %d' % dataset_size)

cycle_gan   #model = create_model(opt)#print(opt.model)

initialization method [normal]    #model.initialize(opt)

initialization method [normal]

initialization method [normal]

initialization method [normal]

---------- Networks initialized -------------

#networks.print_network(self.netG_A)

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

NLayerDiscriminator(

(model): Sequential(

(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

(1): LeakyReLU(0.2, inplace)

(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)

(4): LeakyReLU(0.2, inplace)

(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)

(7): LeakyReLU(0.2, inplace)

(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)

(10): LeakyReLU(0.2, inplace)

(11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))

)

)

Total number of parameters: 2764737

NLayerDiscriminator(

(model): Sequential(

(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

(1): LeakyReLU(0.2, inplace)

(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)

(4): LeakyReLU(0.2, inplace)

(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)

(7): LeakyReLU(0.2, inplace)

(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)

(10): LeakyReLU(0.2, inplace)

(11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))

)

)

Total number of parameters: 2764737

-----------------------------------------------

model [CycleGANModel] was created#print("model [%s] was created" % (model.name()))

create web directory ./checkpoints/girls_cyclegan/web...#print('create web directory %s...' % self.web_dir)

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