HW2 autoregressive flow for images solution issue about deepul HOT 3 OPEN

Also for this question. I'm wondering why in the sample method, the solution populates the samples using normal distributed random variables (which could range from -inf to inf) while the documentation says the sample should be "a numpy array of size (100, H, W, 1) of samples with values in [0, 1], where [0,0.5] represents a black pixel and [0.5,1] represents a white pixel".

samples[:, k, r, c] = torch.normal(loc[torch.arange(n), chosen_centers], log_scale[torch.arange(n), chosen_centers].exp())

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I am also confused by this part. I think the weighted sum version is more reasonable.
I still have a question about this nll function. If my understanding is right, the latent variable here, is from a mixture of gaussians right? $z \sim MoG(\vec\mu, \vec\sigma, \vec weight)$. And the definition of likihood of flow model:
$$NLL= \mathbb{E}_x [ -log p_z(z) - log |det J| ]$$
The value that we calculate in nll function (add the weighted sum part, like you said) is the first part $-log p_z(z)$ right?
log(Normal(loc, log_scale.exp()).log_prob(x.unsqueeze(1).repeat(1,1,self.n_components,1,1)).exp() * weights)

what about the second part $- log |det J|$?
What's more, In this case, the network that we are using is PixelCNN, which is a complex flow. Is there really is a way to calculate the second part $- log |det J|$? Is this 'pixelCNN' flow even invertible?

I have thought about this for days, Thanks guys!

from deepul.

Also for this question. I'm wondering why in the sample method, the solution populates the samples using normal distributed random variables (which could range from -inf to inf) while the documentation says the sample should be "a numpy array of size (100, H, W, 1) of samples with values in [0, 1], where [0,0.5] represents a black pixel and [0.5,1] represents a white pixel".

samples[:, k, r, c] = torch.normal(loc[torch.arange(n), chosen_centers], log_scale[torch.arange(n), chosen_centers].exp())

I checked their q2_save_results function in helper2 class. It seems they use the clip function, only keep values in [0,2] range.
samples = np.clip(samples.astype('float') * 2.0, 0, 1.9999)

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