(self)
| 101 | self.optimizers.append(self.optimizer_F) |
| 102 | |
| 103 | def optimize_parameters(self): |
| 104 | # forward |
| 105 | self.forward() |
| 106 | |
| 107 | # update D |
| 108 | self.set_requires_grad(self.netD, True) |
| 109 | self.optimizer_D.zero_grad() |
| 110 | self.loss_D = self.compute_D_loss() |
| 111 | self.loss_D.backward() |
| 112 | self.optimizer_D.step() |
| 113 | |
| 114 | # update G |
| 115 | self.set_requires_grad(self.netD, False) |
| 116 | self.optimizer_G.zero_grad() |
| 117 | if self.opt.netF == 'mlp_sample': |
| 118 | self.optimizer_F.zero_grad() |
| 119 | self.loss_G = self.compute_G_loss() |
| 120 | self.loss_G.backward() |
| 121 | self.optimizer_G.step() |
| 122 | if self.opt.netF == 'mlp_sample': |
| 123 | self.optimizer_F.step() |
| 124 | |
| 125 | def set_input(self, input): |
| 126 | """Unpack input data from the dataloader and perform necessary pre-processing steps. |
nothing calls this directly
no test coverage detected