I tried out my own dataset in data/mydata (with the A and B folders) but I get the following error:

~/DiscoGAN-pytorch$ python main.py --dataset=mydata --num_gpu=1
[*] MODEL dir: logs/mydata_2017-03-21_14-49-25
[*] PARAM path: logs/mydata_2017-03-21_14-49-25/params.json
Traceback (most recent call last):
  File "main.py", line 41, in <module>
    main(config)
  File "main.py", line 33, in main
    trainer.train()
  File "/home/bart/DiscoGAN-pytorch/trainer.py", line 161, in train
    valid_x_A, valid_x_B = self._get_variable(A_loader.next()), self._get_variable(B_loader.next())
  File "/home/bart/anaconda3/envs/Python36/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 174, in __next__
    return self._process_next_batch(batch)
  File "/home/bart/anaconda3/envs/Python36/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 198, in _process_next_batch
    raise batch.exc_type(batch.exc_msg)
RuntimeError: Traceback (most recent call last):
  File "/home/bart/anaconda3/envs/Python36/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 32, in _worker_loop
    samples = collate_fn([dataset[i] for i in batch_indices])
  File "/home/bart/anaconda3/envs/Python36/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 68, in default_collate
    return torch.stack(batch, 0)
  File "/home/bart/anaconda3/envs/Python36/lib/python3.6/site-packages/torch/functional.py", line 56, in stack
    return torch.cat(list(t.unsqueeze(dim) for t in sequence), dim)
RuntimeError: inconsistent tensor sizes at /data/users/soumith/miniconda2/conda-bld/pytorch-0.1.10_1488755368782/work/torch/lib/TH/generic/THTensorMath.c:2548

Thanks for the PyTorch implementation of DiscoGAN. I am having troubles running it with multiple GPUs though. I think the first problem is in the config.py file, where it casts the num_gpu argument to a bool instead of an int. https://github.com/carpedm20/DiscoGAN-pytorch/blob/2feae821dcea201b5461db58e2438af97f11cb63/config.py#L53 But even fixing that, the code will only run when --num_gpu=1 and not when --num_gpu=2. Here's the traceback error:

Traceback (most recent call last):
  File "main.py", line 41, in <module>
    main(config)
  File "main.py", line 33, in main
    trainer.train()
  File "/home/***/Documents/DiscoGAN-pytorch-master/trainer.py", line 187, in train
    x_AB = self.G_AB(x_A).detach()
  File "/home/***/.pyenv/versions/2.7.13/lib/python2.7/site-packages/torch/nn/modules/module.py", line 206, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/***/Documents/DiscoGAN-pytorch-master/models.py", line 45, in forward
    return nn.parallel.data_parallel(self.main, x, gpu_ids)
  File "/home/***/.pyenv/versions/2.7.13/lib/python2.7/site-packages/torch/nn/parallel/data_parallel.py", line 101, in data_parallel
    replicas = replicate(module, device_ids[:len(inputs)])
  File "/home/***/.pyenv/versions/2.7.13/lib/python2.7/site-packages/torch/nn/parallel/replicate.py", line 10, in replicate
    params = list(network.parameters())
AttributeError: 'function' object has no attribute 'parameters'

Any idea what could be the cause?

(base) D:\DiscoGAN>python main.py --dataset=siys2simk --input_scale_size 256 --batch_size 4 --a_grayscale True --b_grayscale True --num_worker 1 --num_gpu=0
[*] MODEL dir: ./logs\siys2simk_2022-08-26_11-17-52
[*] PARAM path: ./logs\siys2simk_2022-08-26_11-17-52\params.json
  0%|                                                                                                                                                                                         | 0/500000 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "D:\DiscoGAN\main.py", line 41, in <module>
    main(config)
  File "D:\DiscoGAN\main.py", line 33, in main
    trainer.train()
  File "D:\DiscoGAN\trainer.py", line 200, in train
    l_d_A_real, l_d_A_fake = bce(self.D_A(x_A).squeeze(1), real_tensor), bce(self.D_A(x_BA).squeeze(1), fake_tensor)
  File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\loss.py", line 613, in forward
    return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)
  File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\functional.py", line 3074, in binary_cross_entropy
    raise ValueError(
ValueError: Using a target size (torch.Size([4])) that is different to the input size (torch.Size([4, 169])) is deprecated. Please ensure they have the same size.

Hello Sir,

I downloaded your code and maps-dataset.. And when I started I met some error.

...
Traceback (most recent call last):
  File "main.py", line 41, in <module>
    main(config)
  File "main.py", line 33, in main
    trainer.train()
  File "/itsme/TESTBOARD/additional_networks/GAN/pytorch_DiscoGAN_carpedm20/trainer.py", line 247, in train
    format(step, self.max_step, l_d.data[0], l_g.data[0]))
IndexError: invalid index of a 0-dim tensor. Use tensor.item() to convert a 0-dim tensor to a Python number

Thanks..

Once I set '-- input_scale_size' not to 64, an error will be reported. It seems that the discriminator cannot adapt to the size of the input image. How did you solve the problem? "ValueError: Target and input must have the same number of elements. target nelement (2) != input nelement (338)"

getting this message:

0%| | 1/50000 [00:03<55:08:42, 3.97s/it]C:\Users\Shadow\Anaconda3\lib\site-packages\torch\nn\modules\loss.py:512: UserWarning: Using a target size (torch.Size([200])) that is different to the input size (torch.Size([200, 1])) is deprecated. Please ensure they have the same size.

DiscoGAN paper they assert that "to avoid costly pairing, we address the task of discovering cross-domain relations given unpaired data" But, I don't know why this implementation of DiscoGAN requires paired data? (as far as I know/see, in the dataset downloaded ) Or, is it just to simplify the input process?