(mf) D:\zyn\project\FCAGAN-main>python train.py --dataroot I:\字体数据集\FTrans-dataset-font\font\facgan-train800+notosans --model FCA_model --name testfca --no_dropout --batch_size 64  --gpu_ids 0 --verbose
----------------- Options ---------------
               batch_size: 64                                   [default: 256]
                    beta1: 0.5
          checkpoints_dir: ./checkpoints
           continue_train: False
                 dataroot: I:\字体数据集\FTrans-dataset-font\font\facgan-train800+notosans      [default: None]
             dataset_mode: font
                direction: english2chinese
                    dis_2: True
              display_env: main
             display_freq: 51200
               display_id: 1
            display_ncols: 10
             display_port: 8097
           display_server: http://localhost
          display_winsize: 64
                    epoch: latest
              epoch_count: 1
                 gan_mode: hinge
                gan_modes: hinge2
                  gpu_ids: 0
                init_gain: 0.02
                init_type: normal
                  isTrain: True                                 [default: None]
                lambda_L1: 100.0
           lambda_content: 1.0
             lambda_style: 1.0
                load_iter: 0                                    [default: 0]
                load_size: 64
                       lr: 0.0002
           lr_decay_iters: 50
                lr_policy: linear
         max_dataset_size: inf
                    model: FCA_model                            [default: FCA]
                 n_epochs: 10
           n_epochs_decay: 10
               n_layers_D: 3
                     name: testfca                              [default: experiment_name]
                      ndf: 64
                     netD: basic_64
                   netD_s: basic_64_c
                     netG: FCA
                      ngf: 64
               no_dropout: True                                 [default: False]
                  no_html: False
                     norm: batch
              num_threads: 4
                    phase: train
                pool_size: 0
               print_freq: 51200
             save_by_iter: False
          save_epoch_freq: 1
         save_latest_freq: 5000000
            style_channel: 6
                   suffix:
         update_html_freq: 51200
        use_spectral_norm: True
                  verbose: True                                 [default: False]
----------------- End -------------------
dataset [FontDataset] was created
The number of training images = 738251
initialize network with normal
initialize network with normal
initialize network with normal
model [FCA_Model] was created
---------- Networks initialized -------------
DataParallel(
  (module): FCA(
    (style_encoder): FCA_Encoder(
      (model): Sequential(
        (0): ReflectionPad2d((3, 3, 3, 3))
        (1): Conv2d(1, 64, kernel_size=(7, 7), stride=(1, 1), bias=False)
        (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (3): ReLU(inplace=True)
        (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (5): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (6): ReLU(inplace=True)
        (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (9): ReLU(inplace=True)
      )
    )
    (content_encoder): FCA_Encoder(
      (model): Sequential(
        (0): ReflectionPad2d((3, 3, 3, 3))
        (1): Conv2d(1, 64, kernel_size=(7, 7), stride=(1, 1), bias=False)
        (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (3): ReLU(inplace=True)
        (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (5): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (6): ReLU(inplace=True)
        (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (9): ReLU(inplace=True)
      )
    )
    (decoder): FCA_Decoder(
      (model): Sequential(
        (0): ResnetBlock(
          (conv_block): Sequential(
            (0): ReflectionPad2d((1, 1, 1, 1))
            (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)
            (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (3): ReLU(inplace=True)
            (4): ReflectionPad2d((1, 1, 1, 1))
            (5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)
            (6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): ResnetBlock(
          (conv_block): Sequential(
            (0): ReflectionPad2d((1, 1, 1, 1))
            (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)
            (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (3): ReLU(inplace=True)
            (4): ReflectionPad2d((1, 1, 1, 1))
            (5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)
            (6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (2): ResnetBlock(
          (conv_block): Sequential(
            (0): ReflectionPad2d((1, 1, 1, 1))
            (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)
            (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (3): ReLU(inplace=True)
            (4): ReflectionPad2d((1, 1, 1, 1))
            (5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)
            (6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (3): ResnetBlock(
          (conv_block): Sequential(
            (0): ReflectionPad2d((1, 1, 1, 1))
            (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)
            (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (3): ReLU(inplace=True)
            (4): ReflectionPad2d((1, 1, 1, 1))
            (5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)
            (6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (4): ResnetBlock(
          (conv_block): Sequential(
            (0): ReflectionPad2d((1, 1, 1, 1))
            (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)
            (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (3): ReLU(inplace=True)
            (4): ReflectionPad2d((1, 1, 1, 1))
            (5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)
            (6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (5): ResnetBlock(
          (conv_block): Sequential(
            (0): ReflectionPad2d((1, 1, 1, 1))
            (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)
            (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (3): ReLU(inplace=True)
            (4): ReflectionPad2d((1, 1, 1, 1))
            (5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)
            (6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (6): ConvTranspose2d(512, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
        (7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (8): ReLU(inplace=True)
        (9): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
        (10): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (11): ReLU(inplace=True)
        (12): ReflectionPad2d((3, 3, 3, 3))
        (13): Conv2d(128, 1, kernel_size=(7, 7), stride=(1, 1))
        (14): Tanh()
      )
    )
    (local_atten_1): FCA_Atten(
      (self_atten): FCA_attention_module(
        (model16_16): Sequential(
          (0): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=256)
          (1): Conv2d(256, 256, kernel_size=(11, 11), stride=(1, 1), padding=(15, 15), dilation=(3, 3), groups=256)
          (2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        )
        (model8_8): Sequential(
          (0): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=256)
          (1): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3), groups=256)
          (2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        )
        (model4_4): Sequential(
          (0): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=256)
          (1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (attention): Linear(in_features=256, out_features=100, bias=True)
      (context_vec): Linear(in_features=100, out_features=1, bias=False)
      (softmax): Softmax(dim=1)
      (elu): ELU(alpha=1.0)
    )
    (local_atten_2): FCA_Atten(
      (self_atten): FCA_attention_module(
        (model16_16): Sequential(
          (0): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=256)
          (1): Conv2d(256, 256, kernel_size=(11, 11), stride=(1, 1), padding=(15, 15), dilation=(3, 3), groups=256)
          (2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        )
        (model8_8): Sequential(
          (0): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=256)
          (1): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3), groups=256)
          (2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        )
        (model4_4): Sequential(
          (0): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=256)
          (1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (attention): Linear(in_features=256, out_features=100, bias=True)
      (context_vec): Linear(in_features=100, out_features=1, bias=False)
      (softmax): Softmax(dim=1)
      (elu): ELU(alpha=1.0)
    )
    (local_atten_3): FCA_Atten(
      (self_atten): FCA_attention_module(
        (model16_16): Sequential(
          (0): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=256)
          (1): Conv2d(256, 256, kernel_size=(11, 11), stride=(1, 1), padding=(15, 15), dilation=(3, 3), groups=256)
          (2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        )
        (model8_8): Sequential(
          (0): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=256)
          (1): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3), groups=256)
          (2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        )
        (model4_4): Sequential(
          (0): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=256)
          (1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (attention): Linear(in_features=256, out_features=100, bias=True)
      (context_vec): Linear(in_features=100, out_features=1, bias=False)
      (softmax): Softmax(dim=1)
      (elu): ELU(alpha=1.0)
    )
    (layer_atten): FCA_Layer_Atten(
      (fc): Linear(in_features=4096, out_features=3, bias=True)
      (softmax): Softmax(dim=1)
    )
    (downsample_1): Sequential(
      (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (downsample_2): Sequential(
      (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
  )
)
[Network G] Total number of parameters : 32.605 M
DataParallel(
  (module): NLayerDiscriminatorSstyle(
    (nor): Conv2d(256, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
    (model2): Sequential(
      (0): Conv2d(2, 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), bias=False)
      (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): LeakyReLU(negative_slope=0.2, inplace=True)
      (5): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (7): LeakyReLU(negative_slope=0.2, inplace=True)
    )
    (downsample2): Sequential(
      (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (fc2): Linear(in_features=4096, out_features=3, bias=True)
    (softmax2): Softmax(dim=1)
  )
)
[Network D_content] Total number of parameters : 1.036 M
DataParallel(
  (module): NLayerDiscriminatorSstyle(
    (nor): Conv2d(256, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
    (model2): Sequential(
      (0): Conv2d(7, 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), bias=False)
      (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): LeakyReLU(negative_slope=0.2, inplace=True)
      (5): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (7): LeakyReLU(negative_slope=0.2, inplace=True)
    )
    (downsample2): Sequential(
      (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (fc2): Linear(in_features=4096, out_features=3, bias=True)
    (softmax2): Softmax(dim=1)
  )
)
[Network D_style] Total number of parameters : 1.041 M
-----------------------------------------------
Setting up a new session...
create web directory ./checkpoints\testfca\web...
[WinError 10054] 远程主机强迫关闭了一个现有的连接。
Visdom.setup_socket.<locals>.on_close() takes 1 positional argument but 3 were given
Traceback (most recent call last):
  File "D:\zyn\project\FCAGAN-main\train.py", line 25, in <module>
    for i, data in enumerate(dataset):  # inner loop within one epoch
  File "D:\zyn\project\FCAGAN-main\data\__init__.py", line 54, in __iter__
    for i, data in enumerate(self.dataloader):
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 634, in __next__
    data = self._next_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1329, in _next_data
    idx, data = self._get_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1295, in _get_data
    success, data = self._try_get_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1133, in _try_get_data
    data = self._data_queue.get(timeout=timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\queues.py", line 113, in get
    if not self._poll(timeout):
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 257, in poll
    return self._poll(timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 330, in _poll
    return bool(wait([self], timeout))
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 879, in wait
    ready_handles = _exhaustive_wait(waithandle_to_obj.keys(), timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 811, in _exhaustive_wait
    res = _winapi.WaitForMultipleObjects(L, False, timeout)
KeyboardInterrupt
^C
(mf) D:\zyn\project\FCAGAN-main>python train.py --dataroot I:\字体数据集\FTrans-dataset-font\font\facgan-train800+notosans --model FCA_model --name testfca --no_dropout --batch_size 64  --gpu_ids 0          
----------------- Options ---------------
               batch_size: 64                                   [default: 256]
                    beta1: 0.5
          checkpoints_dir: ./checkpoints
           continue_train: False
                 dataroot: I:\字体数据集\FTrans-dataset-font\font\facgan-train800+notosans      [default: None]
             dataset_mode: font
                direction: english2chinese
                    dis_2: True
              display_env: main
             display_freq: 51200
               display_id: 0
            display_ncols: 10
             display_port: 8097
           display_server: http://localhost
          display_winsize: 64
                    epoch: latest
              epoch_count: 1
                 gan_mode: hinge
                gan_modes: hinge2
                  gpu_ids: 0
                init_gain: 0.02
                init_type: normal
                  isTrain: True                                 [default: None]
                lambda_L1: 100.0
           lambda_content: 1.0
             lambda_style: 1.0
                load_iter: 0                                    [default: 0]
                load_size: 64
                       lr: 0.0002
           lr_decay_iters: 50
                lr_policy: linear
         max_dataset_size: inf
                    model: FCA_model                            [default: FCA]
                 n_epochs: 10
           n_epochs_decay: 10
               n_layers_D: 3
                     name: testfca                              [default: experiment_name]
                      ndf: 64
                     netD: basic_64
                   netD_s: basic_64_c
                     netG: FCA
                      ngf: 64
               no_dropout: True                                 [default: False]
                  no_html: False
                     norm: batch
              num_threads: 4
                    phase: train
                pool_size: 0
               print_freq: 51200
             save_by_iter: False
          save_epoch_freq: 1
         save_latest_freq: 5000000
            style_channel: 6
                   suffix:
         update_html_freq: 51200
        use_spectral_norm: True
                  verbose: False
----------------- End -------------------
dataset [FontDataset] was created
The number of training images = 738251
initialize network with normal
initialize network with normal
initialize network with normal
model [FCA_Model] was created
---------- Networks initialized -------------
[Network G] Total number of parameters : 32.605 M
[Network D_content] Total number of parameters : 1.036 M
[Network D_style] Total number of parameters : 1.041 M
-----------------------------------------------
create web directory ./checkpoints\testfca\web...
Traceback (most recent call last):
  File "D:\zyn\project\FCAGAN-main\train.py", line 25, in <module>
    for i, data in enumerate(dataset):  # inner loop within one epoch
  File "D:\zyn\project\FCAGAN-main\data\__init__.py", line 54, in __iter__
    for i, data in enumerate(self.dataloader):
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 634, in __next__
    data = self._next_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1329, in _next_data
    idx, data = self._get_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1295, in _get_data
    success, data = self._try_get_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1133, in _try_get_data
    data = self._data_queue.get(timeout=timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\queues.py", line 113, in get
    if not self._poll(timeout):
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 257, in poll
    return self._poll(timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 330, in _poll
    return bool(wait([self], timeout))
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 879, in wait
    ready_handles = _exhaustive_wait(waithandle_to_obj.keys(), timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 811, in _exhaustive_wait
    res = _winapi.WaitForMultipleObjects(L, False, timeout)
KeyboardInterrupt
^C
(mf) D:\zyn\project\FCAGAN-main>python train.py --dataroot I:\字体数据集\FTrans-dataset-font\font\facgan-train800+notosans --model FCA_model --name testfca --no_dropout --batch_size 64  --gpu_ids 0
----------------- Options ---------------
               batch_size: 64                                   [default: 256]
                    beta1: 0.5
          checkpoints_dir: ./checkpoints
           continue_train: False
                 dataroot: I:\字体数据集\FTrans-dataset-font\font\facgan-train800+notosans      [default: None]
             dataset_mode: font
                direction: english2chinese
                    dis_2: True
              display_env: main
             display_freq: 51200
               display_id: 0
            display_ncols: 10
             display_port: 8097
           display_server: http://localhost
          display_winsize: 64
                    epoch: latest
              epoch_count: 1
                 gan_mode: hinge
                gan_modes: hinge2
                  gpu_ids: 0
                init_gain: 0.02
                init_type: normal
                  isTrain: True                                 [default: None]
                lambda_L1: 100.0
           lambda_content: 1.0
             lambda_style: 1.0
                load_iter: 0                                    [default: 0]
                load_size: 64
                       lr: 0.0002
           lr_decay_iters: 50
                lr_policy: linear
         max_dataset_size: inf
                    model: FCA_model                            [default: FCA]
                 n_epochs: 10
           n_epochs_decay: 10
               n_layers_D: 3
                     name: testfca                              [default: experiment_name]
                      ndf: 64
                     netD: basic_64
                   netD_s: basic_64_c
                     netG: FCA
                      ngf: 64
               no_dropout: True                                 [default: False]
                  no_html: False
                     norm: batch
              num_threads: 4
                    phase: train
                pool_size: 0
               print_freq: 51200
             save_by_iter: False
          save_epoch_freq: 1
         save_latest_freq: 5000000
            style_channel: 6
                   suffix:
         update_html_freq: 51200
        use_spectral_norm: True
                  verbose: False
----------------- End -------------------
dataset [FontDataset] was created
The number of training images = 738251
initialize network with normal
initialize network with normal
initialize network with normal
model [FCA_Model] was created
---------- Networks initialized -------------
[Network G] Total number of parameters : 32.605 M
[Network D_content] Total number of parameters : 1.036 M
[Network D_style] Total number of parameters : 1.041 M
-----------------------------------------------
create web directory ./checkpoints\testfca\web...
Traceback (most recent call last):
  File "D:\zyn\project\FCAGAN-main\train.py", line 25, in <module>
    for i, data in enumerate(dataset):  # inner loop within one epoch
  File "D:\zyn\project\FCAGAN-main\data\__init__.py", line 54, in __iter__
    for i, data in enumerate(self.dataloader):
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 634, in __next__
    data = self._next_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1329, in _next_data
    idx, data = self._get_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1295, in _get_data
    success, data = self._try_get_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1133, in _try_get_data
    data = self._data_queue.get(timeout=timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\queues.py", line 113, in get
    if not self._poll(timeout):
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 257, in poll
    return self._poll(timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 330, in _poll
    return bool(wait([self], timeout))
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 879, in wait
    ready_handles = _exhaustive_wait(waithandle_to_obj.keys(), timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 811, in _exhaustive_wait
    res = _winapi.WaitForMultipleObjects(L, False, timeout)
KeyboardInterrupt
^C
(mf) D:\zyn\project\FCAGAN-main>python train.py --dataroot I:\字体数据集\FTrans-dataset-font\font\facgan-train800+notosans --model FCA_model --name testfca --no_dropout --batch_size 128  --gpu_ids 0
----------------- Options ---------------
               batch_size: 128                                  [default: 256]
                    beta1: 0.5
          checkpoints_dir: ./checkpoints
           continue_train: False
                 dataroot: I:\字体数据集\FTrans-dataset-font\font\facgan-train800+notosans      [default: None]
             dataset_mode: font
                direction: english2chinese
                    dis_2: True
              display_env: main
             display_freq: 51200
               display_id: 0
            display_ncols: 10
             display_port: 8097
           display_server: http://localhost
          display_winsize: 64
                    epoch: latest
              epoch_count: 1
                 gan_mode: hinge
                gan_modes: hinge2
                  gpu_ids: 0
                init_gain: 0.02
                init_type: normal
                  isTrain: True                                 [default: None]
                lambda_L1: 100.0
           lambda_content: 1.0
             lambda_style: 1.0
                load_iter: 0                                    [default: 0]
                load_size: 64
                       lr: 0.0002
           lr_decay_iters: 50
                lr_policy: linear
         max_dataset_size: inf
                    model: FCA_model                            [default: FCA]
                 n_epochs: 10
           n_epochs_decay: 10
               n_layers_D: 3
                     name: testfca                              [default: experiment_name]
                      ndf: 64
                     netD: basic_64
                   netD_s: basic_64_c
                     netG: FCA
                      ngf: 64
               no_dropout: True                                 [default: False]
                  no_html: False
                     norm: batch
              num_threads: 4
                    phase: train
                pool_size: 0
               print_freq: 51200
             save_by_iter: False
          save_epoch_freq: 1
         save_latest_freq: 5000000
            style_channel: 6
                   suffix:
         update_html_freq: 51200
        use_spectral_norm: True
                  verbose: False
----------------- End -------------------
dataset [FontDataset] was created
The number of training images = 738251
initialize network with normal
initialize network with normal
initialize network with normal
model [FCA_Model] was created
---------- Networks initialized -------------
[Network G] Total number of parameters : 32.605 M
[Network D_content] Total number of parameters : 1.036 M
[Network D_style] Total number of parameters : 1.041 M
-----------------------------------------------
create web directory ./checkpoints\testfca\web...
(epoch: 1, iters: 51200, time: 0.004, data: 20.410) G_GAN: 1.552 G_L1: 37.945 D_content: 0.254 D_style: 0.419 
(epoch: 1, iters: 102400, time: 0.004, data: 0.003) G_GAN: 1.143 G_L1: 37.880 D_content: 0.128 D_style: 0.232 
Traceback (most recent call last):
  File "D:\zyn\project\FCAGAN-main\train.py", line 25, in <module>
    for i, data in enumerate(dataset):  # inner loop within one epoch
  File "D:\zyn\project\FCAGAN-main\data\__init__.py", line 54, in __iter__
    for i, data in enumerate(self.dataloader):
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 634, in __next__
    data = self._next_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1329, in _next_data
    idx, data = self._get_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1295, in _get_data
    success, data = self._try_get_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1133, in _try_get_data
    data = self._data_queue.get(timeout=timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\queues.py", line 113, in get
    if not self._poll(timeout):
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 257, in poll
    return self._poll(timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 330, in _poll
    return bool(wait([self], timeout))
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 879, in wait
    ready_handles = _exhaustive_wait(waithandle_to_obj.keys(), timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 811, in _exhaustive_wait
    res = _winapi.WaitForMultipleObjects(L, False, timeout)
KeyboardInterrupt
^C
(mf) D:\zyn\project\FCAGAN-main>python train.py --dataroot I:\字体数据集\FTrans-dataset-font\font\facgan-train800+notosans --model FCA_model --name testfca --no_dropout --batch_size 128  --gpu_ids 0
----------------- Options ---------------
               batch_size: 128                                  [default: 256]
                    beta1: 0.5
          checkpoints_dir: ./checkpoints
           continue_train: False
                 dataroot: I:\字体数据集\FTrans-dataset-font\font\facgan-train800+notosans      [default: None]
             dataset_mode: font
                direction: english2chinese
                    dis_2: True
              display_env: main
             display_freq: 51200
               display_id: 1
            display_ncols: 10
             display_port: 8097
           display_server: http://localhost
          display_winsize: 64
                    epoch: latest
              epoch_count: 1
                 gan_mode: hinge
                gan_modes: hinge2
                  gpu_ids: 0
                init_gain: 0.02
                init_type: normal
                  isTrain: True                                 [default: None]
                lambda_L1: 100.0
           lambda_content: 1.0
             lambda_style: 1.0
                load_iter: 0                                    [default: 0]
                load_size: 64
                       lr: 0.0002
           lr_decay_iters: 50
                lr_policy: linear
         max_dataset_size: inf
                    model: FCA_model                            [default: FCA]
                 n_epochs: 10
           n_epochs_decay: 10
               n_layers_D: 3
                     name: testfca                              [default: experiment_name]
                      ndf: 64
                     netD: basic_64
                   netD_s: basic_64_c
                     netG: FCA
                      ngf: 64
               no_dropout: True                                 [default: False]
                  no_html: False
                     norm: batch
              num_threads: 4
                    phase: train
                pool_size: 0
               print_freq: 51200
             save_by_iter: False
          save_epoch_freq: 1
         save_latest_freq: 5000000
            style_channel: 6
                   suffix:
         update_html_freq: 51200
        use_spectral_norm: True
                  verbose: False
----------------- End -------------------
dataset [FontDataset] was created
The number of training images = 738251
initialize network with normal
initialize network with normal
initialize network with normal
model [FCA_Model] was created
---------- Networks initialized -------------
[Network G] Total number of parameters : 32.605 M
[Network D_content] Total number of parameters : 1.036 M
[Network D_style] Total number of parameters : 1.041 M
-----------------------------------------------
Setting up a new session...
create web directory ./checkpoints\testfca\web...
[WinError 10054] 远程主机强迫关闭了一个现有的连接。
Visdom.setup_socket.<locals>.on_close() takes 1 positional argument but 3 were given
Traceback (most recent call last):
  File "D:\zyn\project\FCAGAN-main\train.py", line 25, in <module>
    for i, data in enumerate(dataset):  # inner loop within one epoch
  File "D:\zyn\project\FCAGAN-main\data\__init__.py", line 54, in __iter__
    for i, data in enumerate(self.dataloader):
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 634, in __next__
    data = self._next_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1329, in _next_data
    idx, data = self._get_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1295, in _get_data
    success, data = self._try_get_data()
  File "C:\ProgramData\anaconda3\envs\mf\lib\site-packages\torch\utils\data\dataloader.py", line 1133, in _try_get_data
    data = self._data_queue.get(timeout=timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\queues.py", line 113, in get
    if not self._poll(timeout):
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 257, in poll
    return self._poll(timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 330, in _poll
    return bool(wait([self], timeout))
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 879, in wait
    ready_handles = _exhaustive_wait(waithandle_to_obj.keys(), timeout)
  File "C:\ProgramData\anaconda3\envs\mf\lib\multiprocessing\connection.py", line 811, in _exhaustive_wait
    res = _winapi.WaitForMultipleObjects(L, False, timeout)
KeyboardInterrupt
^C
(mf) D:\zyn\project\FCAGAN-main>python train.py --dataroot I:\字体数据集\FTrans-dataset-font\font\facgan-train800+notosans --model FCA_model --name testfca --no_dropout --batch_size 256  --gpu_ids 0
----------------- Options ---------------
               batch_size: 256
                    beta1: 0.5
          checkpoints_dir: ./checkpoints
           continue_train: False
                 dataroot: I:\字体数据集\FTrans-dataset-font\font\facgan-train800+notosans      [default: None]
             dataset_mode: font
                direction: english2chinese
                    dis_2: True
              display_env: main
             display_freq: 51200
               display_id: 1
            display_ncols: 10
             display_port: 8097
           display_server: http://localhost
          display_winsize: 64
                    epoch: latest
              epoch_count: 1
                 gan_mode: hinge
                gan_modes: hinge2
                  gpu_ids: 0
                init_gain: 0.02
                init_type: normal
                  isTrain: True                                 [default: None]
                lambda_L1: 100.0
           lambda_content: 1.0
             lambda_style: 1.0
                load_iter: 0                                    [default: 0]
                load_size: 64
                       lr: 0.0002
           lr_decay_iters: 50
                lr_policy: linear
         max_dataset_size: inf
                    model: FCA_model                            [default: FCA]
                 n_epochs: 10
           n_epochs_decay: 10
               n_layers_D: 3
                     name: testfca                              [default: experiment_name]
                      ndf: 64
                     netD: basic_64
                   netD_s: basic_64_c
                     netG: FCA
                      ngf: 64
               no_dropout: True                                 [default: False]
                  no_html: False
                     norm: batch
              num_threads: 4
                    phase: train
                pool_size: 0
               print_freq: 51200
             save_by_iter: False
          save_epoch_freq: 1
         save_latest_freq: 5000000
            style_channel: 6
                   suffix:
         update_html_freq: 51200
        use_spectral_norm: True
                  verbose: False
----------------- End -------------------
dataset [FontDataset] was created
The number of training images = 738251
initialize network with normal
initialize network with normal
initialize network with normal
model [FCA_Model] was created
---------- Networks initialized -------------
[Network G] Total number of parameters : 32.605 M
[Network D_content] Total number of parameters : 1.036 M
[Network D_style] Total number of parameters : 1.041 M
-----------------------------------------------
Setting up a new session...
create web directory ./checkpoints\testfca\web...
(epoch: 1, iters: 51200, time: 0.014, data: 24.985) G_GAN: 1.633 G_L1: 37.810 D_content: 0.141 D_style: 0.310 
(epoch: 1, iters: 102400, time: 0.016, data: 10.222) G_GAN: 1.219 G_L1: 35.840 D_content: 0.118 D_style: 0.308 
(epoch: 1, iters: 153600, time: 0.015, data: 3.148) G_GAN: 0.245 G_L1: 35.368 D_content: 0.259 D_style: 0.131 
(epoch: 1, iters: 204800, time: 0.015, data: 0.007) G_GAN: 0.233 G_L1: 36.092 D_content: 0.256 D_style: 0.465 
(epoch: 1, iters: 256000, time: 0.015, data: 0.014) G_GAN: -0.626 G_L1: 35.138 D_content: 0.975 D_style: 0.618 
(epoch: 1, iters: 307200, time: 0.016, data: 0.009) G_GAN: 0.922 G_L1: 34.100 D_content: 0.300 D_style: 0.446 
(epoch: 1, iters: 358400, time: 0.015, data: 0.006) G_GAN: 0.431 G_L1: 32.767 D_content: 0.289 D_style: 0.386 
(epoch: 1, iters: 409600, time: 0.016, data: 0.010) G_GAN: 1.354 G_L1: 34.544 D_content: 0.925 D_style: 0.673 
