Runtime error related to tensor shapes when training ProGAN

[rvdmaazen]

I am trying to train a pg.ProGAN module on my created dataset, however when trying to use the train function I run into the following error:

/opt/venv/lib/python3.7/site-packages/pro_gan_pytorch/Losses.py in __gradient_penalty(self, real_samps, fake_samps, height, alpha, reg_lambda)
    124 
    125         # create the merge of both real and fake samples
--> 126         merged = epsilon * real_samps + ((1 - epsilon) * fake_samps)
    127         merged.requires_grad_(True)
    128 

RuntimeError: The size of tensor a (16) must match the size of tensor b (4) at non-singleton dimension 3

Looking at the complete error it seems that the shapes of the real samples and fake sample are not the same. I am using the following training code using pro-gan-pth version 2.1.1 installed using pip:

import pro_gan_pytorch.PRO_GAN as pg

device = "cuda" if torch.cuda.is_available() else "cpu"
dataset = ImageDataset('full_dataset.hdf5')

depth = 4
num_epochs = [10, 10, 10, 10]
fade_ins = [50, 50, 50, 50]
batch_sizes = [128, 128, 128, 128]
latent_size = 128

pro_gan = pg.ProGAN(depth=depth, latent_size=latent_size, device=device)

pro_gan.train(
    dataset=dataset, 
    epochs=num_epochs,
    fade_in_percentage=fade_ins,
    batch_sizes=batch_sizes,
    num_workers=1
)

[rvdmaazen]

The issue was related to the size of the images I was training the network on. The dataloader was outputting 128x128 resolution images while the generator (using a depth of 4) was outputting 16x16 resolution images. Increasing the depth solved the issue.

[akanimax]

@rvdmaazen, Glad to know it was solved. Please feel free to reopen if you face any issue.

cheers :beers:! @akanimax