Hello community,
I am trying to implement a modelbased GAN, where I try to predict simulation parameters from the generator and use the generator’s output to calculate simulations from a custom monte Carlo simulation function. I provide these simulated images to the discriminator with true images to calculate the loss.
I then try to update both generator and discriminator based on the losses calculated in the above stage.
The problem with this is, weights of the generator are not updating.

Is this approach feasible for PyTorch, where I have to break the graph on purpose?

Is there a better way to address this issue?

Is it possible if I define my simulation script as a custom torch layer?
I am attaching a simple example flow for reference.
class Discriminator(nn.Module):
def __init__(self, in_features):
super().__init__()
self.disc = nn.Sequential(
nn.Linear(in_features, 128),
nn.ReLU(),
nn.Linear(128, 1),
nn.Sigmoid(),
)
def forward(self, x):
return self.disc(x)
class Generator(nn.Module):
def __init__(self, n_params, scalar, test_transform=None):
super().__init__()
self.scalar = scalar
self.test_transform = test_transform
self.gen = nn.Sequential(
nn.Linear(n_params, 2 * n_params),
nn.ReLU(),
nn.Linear(2 * n_params, 2 * n_params),
nn.ReLU(),
nn.Linear(2 * n_params, n_params),
nn.Tanh(),
)
def forward(self, x):
p1 = self.gen(x)
params = self.scalar.inverse_transform(p1.clone().detach().numpy())
params = p1 + torch.tensor([100, 1.0e12, 100, 1e3, 1 * math.pi / 180])
# Generate simulation
im = simulate_image(params[0])
fake = self.test_transform(image=np.swapaxes(np.array(im), 1, 1))
fake = torch.from_numpy(fake["image"])
fake = fake.contiguous()
return fake
I also tried without converting my p1 Tensor to numpy and directly giving tensor to my simulation function. But that also didn’t work.