How to give more training time to generator and less to disciminator in GAN-like model

[MarcoAlbertoGrimaldi]

Hi,
as title I was wandering how I can train more one sub-model than another.
Like 5 epochs on generator and 1 on discriminator
My code is:

` def configure_optimizers(self):
#stuff
return [optimizer_G, optimizer_D], []

def training_step(self, train_batch, batch_idx, optimizer_idx):

    grid_real, real = train_batch
    grid_fake = self.generator(real)

    valid = torch.ones(self.patch_dim, requires_grad=False, device=self.device).type_as(real)

    if optimizer_idx == 0:
        sample_img = grid_fake[0]
        self.logger.experiment.add_image('generated_images', sample_img, 0)
        g_loss = self.gan_loss(self.discriminator(grid_fake, grid_real),
                               valid) + self.hparams.lambda_multiplier * self.pixel_loss(grid_fake, grid_real)
        self.log('generator_loss', g_loss)
        return g_loss

    if optimizer_idx == 1:
        invalid = torch.zeros(self.patch_dim, requires_grad=False, device=self.device).type_as(real)
        predict_real = self.discriminator(grid_real, real)
        predict_fake = self.discriminator(grid_fake.detach(), real)
        real_loss = self.gan_loss(predict_real, valid) * 0.5
        fake_loss = self.gan_loss(predict_fake, invalid) * 0.5
        d_loss = real_loss + fake_loss
        self.log('discriminator_loss', d_loss, sync_dist=True)
        return d_loss`

[sakvaua]

if self.current_epoch%5:
#do discriminator