本文主要是介绍torch lighting 设置多个优化器，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
关键代码：
class BodyVQModel(pl.LightningModule):def __init__(self, code_num=2048, embedding_dim=64, num_hiddens=1024, num_residual_layers=2, num_residual_hiddens=512):super().__init__()self.save_hyperparameters()self.automatic_optimization = False...def configure_optimizers(self):body_optimizer = torch.optim.AdamW(self.body_model.parameters(), lr=1e-4, betas=(0.9, 0.999), weight_decay=args.weight_decay)hand_optimizer = torch.optim.AdamW(self.hand_model.parameters(), lr=1e-4, betas=(0.9, 0.999), weight_decay=1e-2)body_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(body_optimizer, mode='min', factor=0.1, patience=200, verbose=True)hand_lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(hand_optimizer, mode='min', factor=0.1, patience=200, verbose=True)return ({"optimizer": body_optimizer, "lr_scheduler": {"scheduler": body_lr_scheduler, "monitor": "val/loss"}},{"optimizer": hand_optimizer, "lr_scheduler": {"scheduler": hand_lr_scheduler, "monitor": "val/loss"}})def training_step(self, batch, batch_idx):opt1, opt2 = self.optimizers()opt1.zero_grad()opt2.zero_grad()loss_dict = {}loss_b, loss_dict = self._calc_loss(self.body_model, batch['motion'][:, :, upper_body_idx], loss_dict, prefix="train/body_", is_body=True) # 上本身，(B, T=88, 39)loss_h, loss_dict = self._calc_loss(self.hand_model, batch['motion'][:, :, hands_idx], loss_dict, prefix="train/hand_", is_body=False) # 手部，(B, T=88, 90)loss = loss_b + loss_hself.log_dict(loss_dict)self.log("train/loss", loss)rec_loss = loss_dict['train/body_rec_loss'] + loss_dict['train/hand_rec_loss']self.log("train/rec_loss", rec_loss)# return lossself.manual_backward(loss)# clip gradientsself.clip_gradients(opt1, gradient_clip_val=10, gradient_clip_algorithm="norm")self.clip_gradients(opt2, gradient_clip_val=10, gradient_clip_algorithm="norm")opt1.step()opt2.step()
ref：https://lightning.ai/docs/pytorch/stable/model/manual_optimization.html
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