def _test_g(self, G: ModelTrainer, real): gen_pred = torch.nn.functional.sigmoid(G.forward(real)) G.compute_and_update_test_loss("MSELoss", gen_pred, real) metric = G.compute_metric("MeanSquaredError", gen_pred, real) G.update_test_metric("MeanSquaredError", metric / 32768) return gen_pred
def _train_g(self, G: ModelTrainer, real, backward=True): G.zero_grad() gen_pred = torch.nn.functional.sigmoid(G.forward(real)) loss_G = G.compute_and_update_train_loss("MSELoss", gen_pred, real) metric = G.compute_metric("MeanSquaredError", gen_pred, real) G.update_train_metric("MeanSquaredError", metric / 32768) if backward: loss_G.backward() G.step() return gen_pred