errors caused by network connection about non-stationary_texture_syn HOT 5 CLOSED

I tried to set display_id to 0, but other problem shows up. It looks like a multiprocessing problem.
The problem still occurs at the data preparing phase.
I think it might has something to do with Windows system, I guess pytorch is quite unstable in Windows.
I'll try it in Ubuntu later.

I paste a part of the error trace here for future readers.

------------ Options -------------
N_CRITIC: 5
batchSize: 1
beta1: 0.5
checkpoints_dir: ./checkpoints
continue_train: False
dataroot: ./datasets/half/202
dataset_mode: half_crop
display_freq: 500
display_id: 0
display_port: 8097
display_single_pane_ncols: 0
display_winsize: 256
epoch_count: 1
fineSize: 256
gpu_ids: [0]
gradient_penalty: False
identity: 0.0
input_nc: 3
isTrain: True
lambda_A: 100.0
lambda_B: 10.0
loadSize: 286
lr: 0.0002
max_dataset_size: inf
model: half_style
nThreads: 2
n_layers_D: 4
name: 202_half_style_14x14
ndf: 64
ngf: 64
niter: 50000
niter_decay: 50000
no_dropout: False
no_flip: True
no_html: False
no_lsgan: True
norm: batch
output_nc: 3
padding_type: replicate
phase: train
pool_size: 0
print_freq: 20
resize_or_crop: no
save_epoch_freq: 2000
save_latest_freq: 2000
serial_batches: False
use_style: True
which_direction: AtoB
which_epoch: latest
which_model_netD: n_layers
which_model_netG: resnet_2x_6blocks
-------------- End ----------------
CustomDatasetDataLoader
dataset [HalfDataset] was created
#training images = 1
------------ Options -------------
N_CRITIC: 5
batchSize: 1
beta1: 0.5
checkpoints_dir: ./checkpoints
continue_train: False
dataroot: ./datasets/half/202
dataset_mode: half_crop
display_freq: 500
display_id: 0
display_port: 8097
display_single_pane_ncols: 0
display_winsize: 256
epoch_count: 1
fineSize: 256
gpu_ids: [0]
gradient_penalty: False
identity: 0.0
input_nc: 3
isTrain: True
lambda_A: 100.0
lambda_B: 10.0
loadSize: 286
lr: 0.0002
max_dataset_size: inf
model: half_style
nThreads: 2
n_layers_D: 4
name: 202_half_style_14x14
ndf: 64
ngf: 64
niter: 50000
niter_decay: 50000
no_dropout: False
no_flip: True
no_html: False
no_lsgan: True
norm: batch
output_nc: 3
padding_type: replicate
phase: train
pool_size: 0
print_freq: 20
resize_or_crop: no
save_epoch_freq: 2000
save_latest_freq: 2000
serial_batches: False
use_style: True
which_direction: AtoB
which_epoch: latest
which_model_netD: n_layers
which_model_netG: resnet_2x_6blocks
-------------- End ----------------
CustomDatasetDataLoader
------------ Options -------------
N_CRITIC: 5
batchSize: 1
beta1: 0.5
checkpoints_dir: ./checkpoints
continue_train: False
dataroot: ./datasets/half/202
dataset_mode: half_crop
display_freq: 500
display_id: 0
display_port: 8097
display_single_pane_ncols: 0
display_winsize: 256
epoch_count: 1
fineSize: 256
gpu_ids: [0]
gradient_penalty: False
identity: 0.0
input_nc: 3
isTrain: True
lambda_A: 100.0
lambda_B: 10.0
loadSize: 286
lr: 0.0002
max_dataset_size: inf
model: half_style
nThreads: 2
n_layers_D: 4
name: 202_half_style_14x14
ndf: 64
ngf: 64
niter: 50000
niter_decay: 50000
no_dropout: False
no_flip: True
no_html: False
no_lsgan: True
norm: batch
output_nc: 3
padding_type: replicate
phase: train
pool_size: 0
print_freq: 20
resize_or_crop: no
save_epoch_freq: 2000
save_latest_freq: 2000
serial_batches: False
use_style: True
which_direction: AtoB
which_epoch: latest
which_model_netD: n_layers
which_model_netG: resnet_2x_6blocks
-------------- End ----------------
CustomDatasetDataLoader
dataset [HalfDataset] was created
#training images = 1
half_style
------------ Options -------------
N_CRITIC: 5
batchSize: 1
beta1: 0.5
checkpoints_dir: ./checkpoints
continue_train: False
dataroot: ./datasets/half/202
dataset_mode: half_crop
display_freq: 500
display_id: 0
display_port: 8097
display_single_pane_ncols: 0
display_winsize: 256
epoch_count: 1
fineSize: 256
gpu_ids: [0]
gradient_penalty: False
identity: 0.0
input_nc: 3
isTrain: True
lambda_A: 100.0
lambda_B: 10.0
loadSize: 286
lr: 0.0002
max_dataset_size: inf
model: half_style
nThreads: 2
n_layers_D: 4
name: 202_half_style_14x14
ndf: 64
ngf: 64
niter: 50000
niter_decay: 50000
no_dropout: False
no_flip: True
no_html: False
no_lsgan: True
norm: batch
output_nc: 3
padding_type: replicate
phase: train
pool_size: 0
print_freq: 20
resize_or_crop: no
save_epoch_freq: 2000
save_latest_freq: 2000
serial_batches: False
use_style: True
which_direction: AtoB
which_epoch: latest
which_model_netD: n_layers
which_model_netG: resnet_2x_6blocks
-------------- End ----------------
CustomDatasetDataLoader
dataset [HalfDataset] was created
#training images = 1
half_style
------------ Options -------------
N_CRITIC: 5
batchSize: 1
beta1: 0.5
checkpoints_dir: ./checkpoints
continue_train: False
dataroot: ./datasets/half/202
dataset_mode: half_crop
display_freq: 500
display_id: 0
display_port: 8097
display_single_pane_ncols: 0
display_winsize: 256
epoch_count: 1
fineSize: 256
gpu_ids: [0]
gradient_penalty: False
identity: 0.0
input_nc: 3
isTrain: True
lambda_A: 100.0
lambda_B: 10.0
loadSize: 286
lr: 0.0002
max_dataset_size: inf
model: half_style
nThreads: 2
n_layers_D: 4
name: 202_half_style_14x14
ndf: 64
ngf: 64
niter: 50000
niter_decay: 50000
no_dropout: False
no_flip: True
no_html: False
no_lsgan: True
norm: batch
output_nc: 3
padding_type: replicate
phase: train
pool_size: 0
print_freq: 20
resize_or_crop: no
save_epoch_freq: 2000
save_latest_freq: 2000
serial_batches: False
use_style: True
which_direction: AtoB
which_epoch: latest
which_model_netD: n_layers
which_model_netG: resnet_2x_6blocks
-------------- End ----------------
CustomDatasetDataLoader
dataset [HalfDataset] was created
#training images = 1
half_style
dataset [HalfDataset] was created
#training images = 1
half_style
odict_keys(['conv1_1.weight', 'conv1_1.bias', 'conv1_2.weight', 'conv1_2.bias', 'conv2_1.weight', 'conv2_1.bias', 'conv2_2.weight', 'conv2_2.bias', 'conv3_1.weight', 'conv3_1.bias', 'conv3_2.weight', 'conv3_2.bias', 'conv3_3.weight', 'conv3_3.bias', 'conv3_4.weight', 'conv3_4.bias', 'conv4_1.weight', 'conv4_1.bias', 'conv4_2.weight', 'conv4_2.bias', 'conv4_3.weight', 'conv4_3.bias', 'conv4_4.weight', 'conv4_4.bias', 'conv5_1.weight', 'conv5_1.bias', 'conv5_2.weight', 'conv5_2.bias', 'conv5_3.weight', 'conv5_3.bias',
'conv5_4.weight', 'conv5_4.bias'])
odict_keys(['conv1_1.weight', 'conv1_1.bias', 'conv1_2.weight', 'conv1_2.bias', 'conv2_1.weight', 'conv2_1.bias', 'conv2_2.weight', 'conv2_2.bias', 'conv3_1.weight', 'conv3_1.bias', 'conv3_2.weight', 'conv3_2.bias', 'conv3_3.weight', 'conv3_3.bias', 'conv3_4.weight', 'conv3_4.bias', 'conv4_1.weight', 'conv4_1.bias', 'conv4_2.weight', 'conv4_2.bias', 'conv4_3.weight', 'conv4_3.bias', 'conv4_4.weight', 'conv4_4.bias', 'conv5_1.weight', 'conv5_1.bias', 'conv5_2.weight', 'conv5_2.bias', 'conv5_3.weight', 'conv5_3.bias',
'conv5_4.weight', 'conv5_4.bias'])
---------- Networks initialized -------------
Resnet2XGenerator(
(model): Sequential(
(0): ReflectionPad2d((3, 3, 3, 3))
(1): Conv2d(3, 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)
(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)
(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)
(10): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(11): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(12): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(13): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(14): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(15): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(16): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(17): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(18): ReLU(inplace)
(19): ConvTranspose2d(512, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(20): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(21): ReLU(inplace)
(22): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(23): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(24): ReLU(inplace)
(25): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(26): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(27): ReLU(inplace)
(28): ReflectionPad2d((3, 3, 3, 3))
(29): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
(30): Tanh()
)
)
Total number of parameters: 10202243
NLayerDiscriminator(
(model): Sequential(
(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.2, inplace)
(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)
(5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), 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)
(8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(10): LeakyReLU(negative_slope=0.2, inplace)
(11): Conv2d(512, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1), bias=False)
(12): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(13): LeakyReLU(negative_slope=0.2, inplace)
(14): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
(15): Sigmoid()
)
)
Total number of parameters: 6960961

model [HalfGanStyleModel] was created
create web directory ./checkpoints\202_half_style_14x14\web...
Traceback (most recent call last):
File "", line 1, in
File "D:\Anaconda\envs\Py35\lib\multiprocessing\spawn.py", line 106, in spawn_main
exitcode = _main(fd)
File "D:\Anaconda\envs\Py35\lib\multiprocessing\spawn.py", line 115, in _main
prepare(preparation_data)
File "D:\Anaconda\envs\Py35\lib\multiprocessing\spawn.py", line 226, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "D:\Anaconda\envs\Py35\lib\multiprocessing\spawn.py", line 278, in _fixup_main_from_path
run_name="mp_main")
File "D:\Anaconda\envs\Py35\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "D:\Anaconda\envs\Py35\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "D:\Anaconda\envs\Py35\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "e:\code\non-stationary_texture_syn-master_win\non-stationary_texture_syn-master\train.py", line 29, in
for i, data in enumerate(dataset):
File "D:\Anaconda\envs\Py35\lib\site-packages\torch\utils\data\dataloader.py", line 501, in iter
return _DataLoaderIter(self)
File "D:\Anaconda\envs\Py35\lib\site-packages\torch\utils\data\dataloader.py", line 289, in init
w.start()
File "D:\Anaconda\envs\Py35\lib\multiprocessing\process.py", line 105, in start
self._popen = self._Popen(self)
File "D:\Anaconda\envs\Py35\lib\multiprocessing\context.py", line 212, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "D:\Anaconda\envs\Py35\lib\multiprocessing\context.py", line 313, in _Popen
return Popen(process_obj)
File "D:\Anaconda\envs\Py35\lib\multiprocessing\popen_spawn_win32.py", line 34, in init
prep_data = spawn.get_preparation_data(process_obj._name)
File "D:\Anaconda\envs\Py35\lib\multiprocessing\spawn.py", line 144, in get_preparation_data
_check_not_importing_main()
File "D:\Anaconda\envs\Py35\lib\multiprocessing\spawn.py", line 137, in _check_not_importing_main
is not going to be frozen to produce an executable.''')
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.

    This probably means that you are not using fork to start your
    child processes and you have forgotten to use the proper idiom
    in the main module:

        if __name__ == '__main__':
            freeze_support()
            ...

    The "freeze_support()" line can be omitted if the program
    is not going to be frozen to produce an executable.

odict_keys(['conv1_1.weight', 'conv1_1.bias', 'conv1_2.weight', 'conv1_2.bias', 'conv2_1.weight', 'conv2_1.bias', 'conv2_2.weight', 'conv2_2.bias', 'conv3_1.weight', 'conv3_1.bias', 'conv3_2.weight', 'conv3_2.bias', 'conv3_3.weight', 'conv3_3.bias', 'conv3_4.weight', 'conv3_4.bias', 'conv4_1.weight', 'conv4_1.bias', 'conv4_2.weight', 'conv4_2.bias', 'conv4_3.weight', 'conv4_3.bias', 'conv4_4.weight', 'conv4_4.bias', 'conv5_1.weight', 'conv5_1.bias', 'conv5_2.weight', 'conv5_2.bias', 'conv5_3.weight', 'conv5_3.bias',
'conv5_4.weight', 'conv5_4.bias'])

from non-stationary_texture_syn.

Hi @wzm2256 , can you give a full log about your error?

from non-stationary_texture_syn.

Sure, it's very long and have many duplicate chunks, the debugger says there are 4 thread, take a look

------------ Options -------------
N_CRITIC: 5
batchSize: 1
beta1: 0.5
checkpoints_dir: ./checkpoints
continue_train: False
dataroot: ./datasets/half/202
dataset_mode: half_crop
display_freq: 500
display_id: 1
display_port: 8097
display_single_pane_ncols: 0
display_winsize: 256
epoch_count: 1
fineSize: 256
gpu_ids: [0]
gradient_penalty: False
identity: 0.0
input_nc: 3
isTrain: True
lambda_A: 100.0
lambda_B: 10.0
loadSize: 286
lr: 0.0002
max_dataset_size: inf
model: half_style
nThreads: 2
n_layers_D: 4
name: 202_half_style_14x14
ndf: 64
ngf: 64
niter: 50000
niter_decay: 50000
no_dropout: False
no_flip: True
no_html: False
no_lsgan: True
norm: batch
output_nc: 3
padding_type: replicate
phase: train
pool_size: 0
print_freq: 20
resize_or_crop: no
save_epoch_freq: 2000
save_latest_freq: 2000
serial_batches: False
use_style: True
which_direction: AtoB
which_epoch: latest
which_model_netD: n_layers
which_model_netG: resnet_2x_6blocks
-------------- End ----------------
CustomDatasetDataLoader
dataset [HalfDataset] was created
#training images = 1
half_style
odict_keys(['conv1_1.weight', 'conv1_1.bias', 'conv1_2.weight', 'conv1_2.bias', 'conv2_1.weight', 'conv2_1.bias', 'conv2_2.weight', 'conv2_2.bias', 'conv3_1.weight', 'conv3_1.bias', 'conv3_2.weight', 'conv3_2.bias', 'conv3_3.weight', 'conv3_3.bias', 'conv3_4.weight', 'conv3_4.bias', 'conv4_1.weight', 'conv4_1.bias', 'conv4_2.weight', 'conv4_2.bias', 'conv4_3.weight', 'conv4_3.bias', 'conv4_4.weight', 'conv4_4.bias', 'conv5_1.weight', 'conv5_1.bias', 'conv5_2.weight', 'conv5_2.bias', 'conv5_3.weight', 'conv5_3.bias',
'conv5_4.weight', 'conv5_4.bias'])
---------- Networks initialized -------------
Resnet2XGenerator(
(model): Sequential(
(0): ReflectionPad2d((3, 3, 3, 3))
(1): Conv2d(3, 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)
(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)
(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)
(10): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(11): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(12): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(13): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(14): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(15): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(16): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(17): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(18): ReLU(inplace)
(19): ConvTranspose2d(512, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(20): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(21): ReLU(inplace)
(22): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(23): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(24): ReLU(inplace)
(25): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(26): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(27): ReLU(inplace)
(28): ReflectionPad2d((3, 3, 3, 3))
(29): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
(30): Tanh()
)
)
Total number of parameters: 10202243
NLayerDiscriminator(
(model): Sequential(
(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.2, inplace)
(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)
(5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), 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)
(8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(10): LeakyReLU(negative_slope=0.2, inplace)
(11): Conv2d(512, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1), bias=False)
(12): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(13): LeakyReLU(negative_slope=0.2, inplace)
(14): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
(15): Sigmoid()
)
)
Total number of parameters: 6960961

model [HalfGanStyleModel] was created
Exception in user code:

Traceback (most recent call last):
File "D:\Anaconda\envs\Py35\lib\site-packages\urllib3\connection.py", line 171, in _new_conn
(self._dns_host, self.port), self.timeout, **extra_kw)
File "D:\Anaconda\envs\Py35\lib\site-packages\urllib3\util\connection.py", line 79, in create_connection
raise err
File "D:\Anaconda\envs\Py35\lib\site-packages\urllib3\util\connection.py", line 69, in create_connection
sock.connect(sa)
ConnectionRefusedError: [WinError 10061] No connection could be made because the target machine actively refused it

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "D:\Anaconda\envs\Py35\lib\site-packages\urllib3\connectionpool.py", line 600, in urlopen
chunked=chunked)
File "D:\Anaconda\envs\Py35\lib\site-packages\urllib3\connectionpool.py", line 354, in _make_request
conn.request(method, url, **httplib_request_kw)
File "D:\Anaconda\envs\Py35\lib\http\client.py", line 1107, in request
self._send_request(method, url, body, headers)
File "D:\Anaconda\envs\Py35\lib\http\client.py", line 1152, in _send_request
self.endheaders(body)
File "D:\Anaconda\envs\Py35\lib\http\client.py", line 1103, in endheaders
self._send_output(message_body)
File "D:\Anaconda\envs\Py35\lib\http\client.py", line 934, in _send_output
self.send(msg)
File "D:\Anaconda\envs\Py35\lib\http\client.py", line 877, in send
self.connect()
File "D:\Anaconda\envs\Py35\lib\site-packages\urllib3\connection.py", line 196, in connect
conn = self._new_conn()
File "D:\Anaconda\envs\Py35\lib\site-packages\urllib3\connection.py", line 180, in _new_conn
self, "Failed to establish a new connection: %s" % e)
urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPConnection object at 0x000000001649D400>: Failed to establish a new connection: [WinError 10061] No connection
could be made because the target machine actively refused it

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "D:\Anaconda\envs\Py35\lib\site-packages\requests\adapters.py", line 445, in send
timeout=timeout
File "D:\Anaconda\envs\Py35\lib\site-packages\urllib3\connectionpool.py", line 638, in urlopen
_stacktrace=sys.exc_info()[2])
File "D:\Anaconda\envs\Py35\lib\site-packages\urllib3\util\retry.py", line 398, in increment
raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='localhost', port=8097): Max retries exceeded with url: /env/main (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x000000001649D400>: Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused
it',))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "D:\Anaconda\envs\Py35\lib\site-packages\visdom_init_.py", line 446, in _send
data=json.dumps(msg),
File "D:\Anaconda\envs\Py35\lib\site-packages\requests\api.py", line 112, in post
return request('post', url, data=data, json=json, **kwargs)
File "D:\Anaconda\envs\Py35\lib\site-packages\requests\api.py", line 58, in request
return session.request(method=method, url=url, **kwargs)
File "D:\Anaconda\envs\Py35\lib\site-packages\requests\sessions.py", line 512, in request
resp = self.send(prep, **send_kwargs)
File "D:\Anaconda\envs\Py35\lib\site-packages\requests\sessions.py", line 622, in send
r = adapter.send(request, **kwargs)
File "D:\Anaconda\envs\Py35\lib\site-packages\requests\adapters.py", line 513, in send
raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: HTTPConnectionPool(host='localhost', port=8097): Max retries exceeded with url: /env/main (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x000000001649D400>: Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused it',))
create web directory ./checkpoints\202_half_style_14x14\web...
------------ Options -------------
N_CRITIC: 5
batchSize: 1
beta1: 0.5
checkpoints_dir: ./checkpoints
continue_train: False
dataroot: ./datasets/half/202
dataset_mode: half_crop
display_freq: 500
display_id: 1
display_port: 8097
display_single_pane_ncols: 0
display_winsize: 256
epoch_count: 1
fineSize: 256
gpu_ids: [0]
gradient_penalty: False
identity: 0.0
input_nc: 3
isTrain: True
lambda_A: 100.0
lambda_B: 10.0
loadSize: 286
lr: 0.0002
max_dataset_size: inf
model: half_style
nThreads: 2
n_layers_D: 4
name: 202_half_style_14x14
ndf: 64
ngf: 64
niter: 50000
niter_decay: 50000
no_dropout: False
no_flip: True
no_html: False
no_lsgan: True
norm: batch
output_nc: 3
padding_type: replicate
phase: train
pool_size: 0
print_freq: 20
resize_or_crop: no
save_epoch_freq: 2000
save_latest_freq: 2000
serial_batches: False
use_style: True
which_direction: AtoB
which_epoch: latest
which_model_netD: n_layers
which_model_netG: resnet_2x_6blocks
-------------- End ----------------
CustomDatasetDataLoader
------------ Options -------------
N_CRITIC: 5
batchSize: 1
beta1: 0.5
checkpoints_dir: ./checkpoints
continue_train: False
dataroot: ./datasets/half/202
dataset_mode: half_crop
display_freq: 500
display_id: 1
display_port: 8097
display_single_pane_ncols: 0
display_winsize: 256
epoch_count: 1
fineSize: 256
gpu_ids: [0]
gradient_penalty: False
identity: 0.0
input_nc: 3
isTrain: True
lambda_A: 100.0
lambda_B: 10.0
loadSize: 286
lr: 0.0002
max_dataset_size: inf
model: half_style
nThreads: 2
n_layers_D: 4
name: 202_half_style_14x14
ndf: 64
ngf: 64
niter: 50000
niter_decay: 50000
no_dropout: False
no_flip: True
no_html: False
no_lsgan: True
norm: batch
output_nc: 3
padding_type: replicate
phase: train
pool_size: 0
print_freq: 20
resize_or_crop: no
save_epoch_freq: 2000
save_latest_freq: 2000
serial_batches: False
use_style: True
which_direction: AtoB
which_epoch: latest
which_model_netD: n_layers
which_model_netG: resnet_2x_6blocks
-------------- End ----------------
CustomDatasetDataLoader
dataset [HalfDataset] was created
#training images = 1
half_style
dataset [HalfDataset] was created
#training images = 1
half_style
odict_keys(['conv1_1.weight', 'conv1_1.bias', 'conv1_2.weight', 'conv1_2.bias', 'conv2_1.weight', 'conv2_1.bias', 'conv2_2.weight', 'conv2_2.bias', 'conv3_1.weight', 'conv3_1.bias', 'conv3_2.weight', 'conv3_2.bias', 'conv3_3.weight', 'conv3_3.bias', 'conv3_4.weight', 'conv3_4.bias', 'conv4_1.weight', 'conv4_1.bias', 'conv4_2.weight', 'conv4_2.bias', 'conv4_3.weight', 'conv4_3.bias', 'conv4_4.weight', 'conv4_4.bias', 'conv5_1.weight', 'conv5_1.bias', 'conv5_2.weight', 'conv5_2.bias', 'conv5_3.weight', 'conv5_3.bias',
'conv5_4.weight', 'conv5_4.bias'])
odict_keys(['conv1_1.weight', 'conv1_1.bias', 'conv1_2.weight', 'conv1_2.bias', 'conv2_1.weight', 'conv2_1.bias', 'conv2_2.weight', 'conv2_2.bias', 'conv3_1.weight', 'conv3_1.bias', 'conv3_2.weight', 'conv3_2.bias', 'conv3_3.weight', 'conv3_3.bias', 'conv3_4.weight', 'conv3_4.bias', 'conv4_1.weight', 'conv4_1.bias', 'conv4_2.weight', 'conv4_2.bias', 'conv4_3.weight', 'conv4_3.bias', 'conv4_4.weight', 'conv4_4.bias', 'conv5_1.weight', 'conv5_1.bias', 'conv5_2.weight', 'conv5_2.bias', 'conv5_3.weight', 'conv5_3.bias',
'conv5_4.weight', 'conv5_4.bias'])
---------- Networks initialized -------------
Resnet2XGenerator(
(model): Sequential(
(0): ReflectionPad2d((3, 3, 3, 3))
(1): Conv2d(3, 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)
(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)
(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)
(10): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(11): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(12): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(13): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(14): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(15): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(16): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(17): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(18): ReLU(inplace)
(19): ConvTranspose2d(512, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(20): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(21): ReLU(inplace)
(22): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(23): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(24): ReLU(inplace)
(25): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(26): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(27): ReLU(inplace)
(28): ReflectionPad2d((3, 3, 3, 3))
(29): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
(30): Tanh()
)
)
Total number of parameters: 10202243
NLayerDiscriminator(
(model): Sequential(
(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.2, inplace)
(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)
(5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), 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)
(8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(10): LeakyReLU(negative_slope=0.2, inplace)
(11): Conv2d(512, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1), bias=False)
(12): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(13): LeakyReLU(negative_slope=0.2, inplace)
(14): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
(15): Sigmoid()
)
)
Total number of parameters: 6960961

model [HalfGanStyleModel] was created
---------- Networks initialized -------------
Resnet2XGenerator(
(model): Sequential(
(0): ReflectionPad2d((3, 3, 3, 3))
(1): Conv2d(3, 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)
(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)
(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)
(10): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(11): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(12): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(13): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(14): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(15): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(16): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(17): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(18): ReLU(inplace)
(19): ConvTranspose2d(512, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(20): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(21): ReLU(inplace)
(22): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(23): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(24): ReLU(inplace)
(25): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
(26): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(27): ReLU(inplace)
(28): ReflectionPad2d((3, 3, 3, 3))
(29): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
(30): Tanh()
)
)
Total number of parameters: 10202243
NLayerDiscriminator(
(model): Sequential(
(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.2, inplace)
(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)
(5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), 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)
(8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(10): LeakyReLU(negative_slope=0.2, inplace)
(11): Conv2d(512, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1), bias=False)
(12): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(13): LeakyReLU(negative_slope=0.2, inplace)
(14): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
(15): Sigmoid()
)
)
Total number of parameters: 6960961

model [HalfGanStyleModel] was created

from non-stationary_texture_syn.

It seems to me that this is caused by other problems, not the code. The port 8097 seems to be busy and refused to establish a new connection. I suggest you trying to specify the --display_id option to 0. This will not show the images in the browser and do not establish display connection. You can have a try and see what will happen.

from non-stationary_texture_syn.

I just solved this problem by setting "--nThreads 0".
It turns out the problem is the multiprocessing part in data loading process in pytorch in Windows system, disabling multiprocessing will just do the work.

I'll close this issue now.

from non-stationary_texture_syn.