Why does netG generate different data for same input?

[arunpatro]

In many places, the fake images are generated via:

fake_imgs, _, _, _ = netG(noise, sent_emb, words_embs, mask)

The netG is of class G_NET defined in https://github.com/taoxugit/AttnGAN/blob/master/code/model.py#L397.

When I keep noise, sent_emb, words_embs and mask constant and rerun the generation, I get different fake images. Shouldn't the model be outputting a constant output for a constant input? Is there any stochastic behaviour of the G_NET?

[taoxugit]

The randomness is from class CA_NET, which is for Conditioning Augmentation proposed by this paper.

class CA_NET(nn.Module):
# some code is modified from vae examples
# (https://github.com/pytorch/examples/blob/master/vae/main.py)
def init(self):
super(CA_NET, self).init()
self.t_dim = cfg.TEXT.EMBEDDING_DIM
self.c_dim = cfg.GAN.CONDITION_DIM
self.fc = nn.Linear(self.t_dim, self.c_dim * 4, bias=True)
self.relu = GLU()

def encode(self, text_embedding):
    x = self.relu(self.fc(text_embedding))
    mu = x[:, :self.c_dim]
    logvar = x[:, self.c_dim:]
    return mu, logvar

def reparametrize(self, mu, logvar):
    std = logvar.mul(0.5).exp_()
    if cfg.CUDA:
        eps = torch.cuda.FloatTensor(std.size()).normal_()
    else:
        eps = torch.FloatTensor(std.size()).normal_()
    eps = Variable(eps)
    return eps.mul(std).add_(mu)

def forward(self, text_embedding):
    mu, logvar = self.encode(text_embedding)
    c_code = self.reparametrize(mu, logvar)
    return c_code, mu, logvar