I would like to use the gradient penalty introduced in the NIPS paper https://arxiv.org/abs/1706.04156. In the paper, whenever updating the generator, the gradient penalty for the discriminator parameters is considered.
I know there is a discussion in the previous post (https://discuss.pytorch.org/t/how-to-implement-gradient-penalty-in-pytorch/1656?u=zhangboknight, where the gradient penalty is calculated with respect to the data points
For my case the gradient penalty is calculated with respect to the discriminator parameters:
The authors of the paper gives an implementation through
tf.gradients(V, discriminator_vars) in tensorflow (https://github.com/locuslab/gradient_regularized_gan/blob/c1f61272f6176d4d13016779dbe730b346ae21a1/gaussian-toy-regularized.py#L112)
Is there a similar implementation in Pytorch? Can I still use
torch.autograd.grad to calculate the gradient wrt. discriminator params? Or can I use
.retain_grad() as suggested in https://discuss.pytorch.org/t/how-do-i-calculate-the-gradients-of-a-non-leaf-variable-w-r-t-to-a-loss-function/5112/2?u=zhangboknight? I am not pretty sure. Since this technique helps stabilize the GAN training greatly, the same problem may help others who use Pytorch. Many thanks!