----------------- Options ---------------
batch_size: 4 [default: 1]
beta1: 0.5
checkpoints_dir: ./checkpoints
continue_train: False
crop_size: 256
dataroot: ./selfie2comics [default: None]
dataset_mode: unaligned
direction: AtoB
display_env: main
display_freq: 400
display_id: 1
display_ncols: 4
display_port: 8097
display_server: http://localhost
display_winsize: 256
epoch: latest
epoch_count: 1
gan_mode: lsgan
gpu_ids: 0
init_gain: 0.02
init_type: kaiming [default: normal]
input_nc: 3
isTrain: True [default: None]
lambda_A: 10.0
lambda_B: 10.0
lambda_identity: 0.5
load_iter: 0 [default: 0]
load_size: 256 [default: 286]
lr: 0.0002
lr_decay_iters: 50
lr_policy: linear
max_dataset_size: inf
model: cycle_gan
n_epochs: 100
n_epochs_decay: 100
n_layers_D: 3
name: resnet9_132to400_comics8 [default: experiment_name]
ndf: 64
netD: basic
netG: resnet_9blocks
ngf: 64
no_dropout: True
no_flip: False
no_html: False
norm: instance
num_threads: 4
output_nc: 3
phase: train
pool_size: 50
preprocess: resize_and_crop
print_freq: 100
save_by_iter: False
save_epoch_freq: 5
save_latest_freq: 5000
serial_batches: False
suffix:
update_html_freq: 1000
verbose: False
----------------- End -------------------
dataset [UnalignedDataset] was created
The number of training images = 1530
initialize network with kaiming
initialize network with kaiming
initialize network with kaiming
initialize network with kaiming
model [CycleGANModel] was created
---------- Networks initialized -------------
[Network G_A] Total number of parameters : 11.378 M
[Network G_B] Total number of parameters : 11.378 M
[Network D_A] Total number of parameters : 2.765 M
[Network D_B] Total number of parameters : 2.765 M
-----------------------------------------------
Setting up a new session...
create web directory ./checkpoints/resnet9_132to400_comics8/web...
Traceback (most recent call last):
File "train.py", line 44, in <module>
for i, data in enumerate(dataset): # inner loop within one epoch
File "/content/data/__init__.py", line 90, in __iter__
for i, data in enumerate(self.dataloader):
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 345, in __next__
data = self._next_data()
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 838, in _next_data
return self._process_data(data)
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 881, in _process_data
data.reraise()
File "/usr/local/lib/python3.6/dist-packages/torch/_utils.py", line 394, in reraise
raise self.exc_type(msg)
OSError: Caught OSError in DataLoader worker process 3.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/PIL/ImageFile.py", line 235, in load
s = read(self.decodermaxblock)
File "/usr/local/lib/python3.6/dist-packages/PIL/PngImagePlugin.py", line 659, in load_read
cid, pos, length = self.png.read()
File "/usr/local/lib/python3.6/dist-packages/PIL/PngImagePlugin.py", line 122, in read
length = i32(s)
File "/usr/local/lib/python3.6/dist-packages/PIL/_binary.py", line 82, in i32be
return unpack_from(">I", c, o)[0]
struct.error: unpack_from requires a buffer of at least 4 bytes

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/content/data/unaligned_dataset.py", line 58, in __getitem__
B_img = Image.open(B_path).convert('RGB')
File "/usr/local/lib/python3.6/dist-packages/PIL/Image.py", line 930, in convert
self.load()
File "/usr/local/lib/python3.6/dist-packages/PIL/ImageFile.py", line 241, in load
raise IOError("image file is truncated")
OSError: image file is truncated
Вот такая ошибка при обучении нейронки на google colab. Если запускаю на домашнем пека, то все ок. Я уже запускал нейронку на колабе эту же, с теми же настройками и работало как часы, а шас нихуя. Ебусь уже 2 часа и гугл нихуя не помог. на домашнем пека с теми же либами, настройками обучение идет
что можете посоветовать?