Hi

Thanks for your wonderful code. I read the ACGAN paper and your implementation. I have a query, in the schematic diagram you have shown that c is used along-with x in the Discriminator D. However, in the code, you have not used the class information c while training the D and G(z) as you have done in CGAN ?

[1] Is it wrong to use the class information in the discriminator process ? [2] If it is not required then shouldn't the variable y_fill_ removed ? It seems to be redundant in this case.

Also for the C_fake_loss can we use some random labels or is it required to use those labels that have been sampled from the data_loader (like you have done) ?

Thanks in advance !

In many places like here and here, we have D.train() and G.train(). Is there any particular reason that we need to explicitly set them as True (over and over again) ? The official Pytorch GAN example doesn't seem to do net.train() at all. Thanks!

Thanks for this cool codebase that allows one to use many GANs!

But it seems that only the mnist dataset can be loaded, because the other functions are missing from the utils module. For example, if I issue this command:

python main.py --dataset celebA --gan_type WGAN --epoch 25 --batch_size 64

This results in the traceback:

Traceback (most recent call last):                                                                          │·····
 File "main.py", line 107, in <module>                                                                     │·····
    main()                                                                                                  │·····
  File "main.py", line 86, in main                                                                          │·····
    gan = WGAN(args)                                                                                        │·····
  File "WGAN.py", line 130, in __init__             
│self.data_loader = utils.load_celebA('data/celebA', transform=transforms.Compose(                       
│AttributeError: module 'utils' has no attribute 'load_celebA'  

Thanks for your great work! I have a question about the gradient penalty part in WGAN_GP model. From line 189 to line 194, I know that these codes compute the gradients of the pred_hat with respect to x_hat, but I don't know why you add [0] in the end. Would you please do some explanations to that? Thank you very much!

/home/yangkaixing/miniconda3/envs/GASDA/lib/python3.6/site-packages/torch/nn/functional.py:1806: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead. warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.") Traceback (most recent call last): File "main.py", line 112, in main() File "main.py", line 104, in main gan.train() File "/data/yangkaixing/Generative_Model_Collections/infoGAN.py", line 230, in train info_loss.backward() File "/home/yangkaixing/miniconda3/envs/GASDA/lib/python3.6/site-packages/torch/_tensor.py", line 307, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) File "/home/yangkaixing/miniconda3/envs/GASDA/lib/python3.6/site-packages/torch/autograd/init.py", line 156, in backward allow_unreachable=True, accumulate_grad=True) # allow_unreachable flag RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [64, 3, 4, 4]] is at version 2; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

When I run your CGAN program ,the following error message has occured: D:\ProgramData\Anaconda3\python.exe I:/gupaocode/pytorch-generative-model-collections-master/main.py --dataset mnist --gan_type CGAN --epoch 50 --batch_size 64 Traceback (most recent call last): File "I:/gupaocode/pytorch-generative-model-collections-master/main.py", line 111, in main() File "I:/gupaocode/pytorch-generative-model-collections-master/main.py", line 82, in main gan = CGAN(args) File "I:\gupaocode\pytorch-generative-model-collections-master\CGAN.py", line 94, in init data = self.data_loader.iter().next()[0] File "D:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 517, in next data = self._next_data() File "D:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 557, in _next_data data = self.dataset_fetcher.fetch(index) # may raise StopIteration File "D:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data_utils\fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "D:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data_utils\fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "D:\ProgramData\Anaconda3\lib\site-packages\torchvision\datasets\mnist.py", line 106, in getitem img = self.transform(img) File "D:\ProgramData\Anaconda3\lib\site-packages\torchvision\transforms\transforms.py", line 60, in call img = t(img) File "D:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 889, in call_impl result = self.forward(*input, **kwargs) File "D:\ProgramData\Anaconda3\lib\site-packages\torchvision\transforms\transforms.py", line 221, in forward return F.normalize(tensor, self.mean, self.std, self.inplace) File "D:\ProgramData\Anaconda3\lib\site-packages\torchvision\transforms\functional.py", line 336, in normalize tensor.sub(mean).div(std) RuntimeError: output with shape [1, 28, 28] doesn't match the broadcast shape [3, 28, 28]

Dear author,

Thanks for your friendly implementation of popular GANs. I am reading your implementation of Wasserstein GANs, and notice that the gradient clip operation is placed after optimizer.step as seen in this line. Typically, in pytorch, the gradient operation is placed before the optimizer step.

pls have a check.

Hello @znxlwm,

I have noticed that the GAN models are all defined with model.cuda() that use only one device. Is there any way to train them with multiple gpus so that the input size can be increased without decreasing teh batch size? Thank you.