Sampling

[ChristophJud]

Hi, thank you for sharing your code about the LSA model. Having trained the full model, I'm wondering how I can sample from the LSA? Can I just draw a uniform random vector as z and put it into the CPD estimator?

[DavideA]

Hi Cristoph,

you can sample from the model by sampling a latent vector from the prior (with ancestral sampling) and then decoding it. A snippet should look like this:

def generate_images(model: LSACIFAR10, how_many: int) -> Tuple[torch.Tensor, torch.Tensor]:
    z_samples = torch.zeros(how_many, model.code_length, dtype=torch.float).to('cuda')
    model.eval()
    with torch.no_grad():
        for i in range(0, model.code_length):
            z_dist = model.estimator(z_samples)
            probs = torch.softmax(z_dist[:, :, i]).data
            z_samples[:, i] = torch.multinomial(probs, 1).float().squeeze(1) / model.cpd_channels

        x_samples = model.decoder(z_samples)
    return z_samples, x_samples

In principle, you can sample also from video models, such as the one trained on ShanghaiTech, but the sampling on latent vectors will have two nested loops as the code as the temporal axis as well.

Let me know how it goes.

D

[ChristophJud]

Hi Davide, thank you for your sampling example. So, I started to train mnist which did work quite well. After 200 epochs the samples (first row) and reconstructions (second row) look like:

Then I ran the sampling example. I had to adjust probs = torch.softmax(z_dist[:, :, i]).data to probs = torch.softmax(z_dist[:, :, i], dim=1).data

Here are the samples:

The variation of the samples looks a bit underestimated. It seems as the "memorization" works well but the density estimation is too smooth. Have I over-regularized with lambda=1?

Second: I've tried to train the cifar10 incorporating all the hints I have found in the issues (and the paper/supplement). After 200 epochs, the model did not learn that much:

Interestingly, the reconstructions are almost grayscale.

And the samples:

Have you had similar issues during development?

I'm looking forward to you hints and suggestions!

Christoph

[ChristophJud]

ok, I should have read your supplemental material better. With the right lambda and step size I now get:

and the samples:

Really cool though :) C

[DavideA]

Great!!!

It looked like an over-regularization issue :)

Best, D

[ChristophJud]

Yap :-P

How would you generalize the LSA_cifar10 model to a model with 256x256 input? Just add Down/Up sampling layers or would you also add residual blocks?

Best, C

[ahugj]

@DavideA Hi, thank you for sharing your code about the LSA model. May I get the full training code?

[ahugj]

@ChristophJud Can you share your code?

[ChristophJud]

Hey casgj, do you mean something like:

for epoch in range(max_epochs):
        for i, (x, y) in enumerate(loader):
            opt.zero_grad()

            x = x.to('cuda')

            # Forward pass
            x_r, z, z_dist = model(x)

            # evaluate loss
            loss = criterion(x, x_r, z, z_dist)

            # calculate gradients
            loss.backward()

            # update step
            opt.step()

[ahugj]

@ChristophJud Yes, my training code doesn't work, the accuray and recall is always 0.009%.

[ChristophJud]

I'd recommend to check all hyper-parameters in the supplemental material