rosinality / progressive-gan-pytorch Goto Github PK

Hello,

I don't understand why you have two generators, with one being the running average of the other. I guess you are using the average for evaluation but why not just evaluate the generator that you are training ?

Best,
Ridha.

Thanks for your nice and clean work!

When I tried to understand your code, I found that

b_size = real_image.size(0)
        real_image = real_image.to(device)
        label = label.to(device)
        real_predict = encoder(
            real_image, step=step, alpha=alpha)
        real_predict = real_predict.mean() \
                       - 0.001 * (real_predict ** 2).mean() 

I don't quite understand why the variable real_predict needs to be modified to eal_predict.mean() \- 0.001 * (real_predict ** 2).mean(). Why don't we just modify the discriminator to output a single value? and how do you come out of this formula?

Again, many thanks for your excellent work. I am new to deep learning and GAN so sorry for any inconvenience caused.

First of all, thanks for sharing! Very interesting.

I see that you experimented with SpectralNorm as well, can you share your insights on the effect it had on the training ?

Hi @rosinality,

Thank you for writing this code, and sorry to open an issue on such an old repository, but I was hoping to ask you a question about generating new samples once a model has been trained, as I can't see any code to do so in the repository. I'm currently training a model using the CelebA dataset using your code and am not sure how to generate a new image once it's completed.

Specifically, what values for alpha and step should I pass to my generator call in this code? Can I just pass step=5 and alpha=1?

First of all thanks for your very intuitive implementation.
I was following your code but I saw

    fake_image = generator(
        Variable(torch.randn(b_size, code_size)).cuda(),
        label, step, alpha)
    fake_predict, fake_class_predict = discriminator(
        fake_image, step, alpha)
    fake_predict = fake_predict.mean()
    fake_predict.backward(one)
    real_predict, real_class_predict = discriminator(
    real_image, step, alpha)
    real_predict = real_predict.mean() \
     - 0.001 * (real_predict ** 2).mean()
    real_predict.backward(mone)

But I think so we should be passing one to real_predict and mone to fake_predict because discriminator must realize that fake_images must be given mone label and real_images must be given one label ?
Please explain me

Hi, thanks for your efforts! I was wondering where I should change to adapt the current model to a generation for 256 resolution? Appreciate your reply in advance.

안녕하세요, @rosinality 님

공개해주신 코드 덕분에 공부하는데 있어 많은 도움을 받고있습니다

코드 리뷰 중, 이해가 잘 가지 않는 부분이 있어 질문드립니다

train.py 144, 152 line에서

    real_predict.backward(Tensor(-1.0))
    fake_predict.backward(Tensor(1.0))

backward 뒤에 gradient값이 real_predict에서 -1.0, fake_predict에서 1.0을 주는 이유가 궁금합니다

감사합니다

안녕하세요, 우선 코드 공유를 해주심에 감사드립니다.

중간에 저장된 .model 파일을 이용하여 model을 load하려고 합니다.
혹시 어떻게 load할 수 있을지 알려주시면 감사하겠습니다.

Thank you for grate work !!
I would appreciate it if you could open pre-trained model