I have an image dataset, which splits into 2500 patches. I use patches as VAE input and combine the Z latent space in the encoder.
Now I want to load 2500n as a batch. How can I use pyro.plate()in the guide and model to designate a 2500 mini-batch in a 2500n batch to finish VAE and combine the z in the minibatch?
ps. the input data are arranged in order
class VAE(nn.Module):
# by default our latent space is 50-dimensional
# and we use 400 hidden units
def __init__(self, z_dim=16, hidden_dim=1000, use_cuda=True):
super().__init__()
# create the encoder and decoder networks
self.encoder = Encoder(z_dim, hidden_dim)
self.decoder = Decoder(z_dim, hidden_dim)
if use_cuda:
# calling cuda() here will put all the parameters of
# the encoder and decoder networks into gpu memory
self.cuda()
self.use_cuda = use_cuda
self.z_dim = z_dim
# define the model p(x|z)p(z)
def model(self, x):
# register PyTorch module `decoder` with Pyro
pyro.module("decoder", self.decoder)
with pyro.plate("data", x.shape[0]):
# setup hyperparameters for prior p(z)
z_loc = x.new_zeros(torch.Size((x.shape[0], self.z_dim)))
z_scale = x.new_ones(torch.Size((x.shape[0], self.z_dim)))
# sample from prior (value will be sampled by guide when computing the ELBO)
z = pyro.sample("latent", dist.Normal(z_loc, z_scale).to_event(1))
# decode the latent code z
loc_img = self.decoder(z)
loc_img = loc_img.reshape(-1,200*200)
pyro.sample("obs", dist.Bernoulli(loc_img).to_event(1), obs=x.reshape(-1, 200*200))
# define the guide (i.e. variational distribution) q(z|x)
def guide(self, x):
# register PyTorch module `encoder` with Pyro
pyro.module("encoder", self.encoder)
with pyro.plate("data", x.shape[0]):
# use the encoder to get the parameters used to define q(z|x)
z_loc, z_scale = self.encoder(x)
# sample the latent code z
pyro.sample("latent", dist.Normal(z_loc, z_scale).to_event(1))