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Below code expect a 2 dimensional z_mean (like the vanilla vae case)
z_mean, z_std = rvae.encode(norm_patches)
rvae.decode(z_mean)- when tried with last two latent variables: rvae.decode(z_mean[:, 1:]) -> the decoded patches look very different
Tried a hacky way to decode following: forward_compute_elbo
z_mean, z_std = rvae.encode(norm_patches)
z1, z2, z3 = z_mean[:,0], z_mean[:, 1:3], z_mean[:, 3:]
x_coord_ = rvae.x_coord.expand(norm_patches.shape[0], *rvae.x_coord.size()).cpu()
phi = torch.tensor(z1) # angle
dx = torch.tensor(z2) # translation
dx = (dx * rvae.dx_prior).unsqueeze(1)
x_coord_ = utils.transform_coordinates(x_coord_, phi, dx)
decoded_patches = rvae.decoder_net(x_coord_.to("cuda"), torch.tensor(z3).to("cuda"))It will be useful to have this functionality directly using vae.decode(z_mean)
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