不需要lr_scheduler_step :默认情况下,PyTorch Lightning 根据您指定的'interval'参数( 'epoch'或'step' )处理调度程序的步进。 2. Overriding lr_scheduler_step2. 重写lr_scheduler_step Purpose of lr_scheduler_step: The lr_scheduler_step method in the LightningModule is an optional hook that you can...
classLightningModel(L.LightningModule): ···defconfigure_optimizers(self): optimizer = torch.optim.Adam(self.model.parameters(), lr=0.001) scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)return[optimizer], [scheduler] lightning 会自动执行scheduler.step()。 trainer.init...
y_hat=self(x)loss=nn.functional.cross_entropy(y_hat,y)returnlossdefconfigure_optimizers(self):# 配置优化器和学习率调度器optimizer=torch.optim.Adam(self.parameters(),lr=self.learning_rate)scheduler=torch.optim.lr_scheduler.StepLR(optimizer,step_size=1,gamma=0.1)return[optimizer],[scheduler] 1....
pytorch-lightning-bug 在复现VAE时我用了比较高版本的pytorch-lightning,和pytorch,并不是原版,会出现问题如下: The provided lr scheduler MultiStepLR doesn’t follow PyTorch’s LRScheduler API. You should override the LightningModule.lr_scheduler_step hook with your own logic if you are using a custo...
step_size=5, gamma=0.1) num_epochs = 8 for epoch in range(num_epochs): train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10) lr_scheduler.step() torch.save(model.state_dict(), "mask_rcnn_pedestrian_model.pt") ...
i want to use the the lr scheduler for gan and i am trying to figure out where to give the lr_scheduler_dis.step() if i add it in training_step after loss is updated i am getting an warning which i want to fix Warning: Detected call of l...
test_step() test_epoch_end() 5. 示例 以MNIST为例,将PyTorch版本代码转为PyTorch Lightning。 5.1 PyTorch版本训练MNIST 对于一个PyTorch的代码来说,一般是这样构建网络(源码来自PyTorch中的example库)。 classNet(nn.Module):def__init__(self):super(Net, self).__init__() ...
You should override the 'LightningModule.lr_scedyler_step hook with your own logic if you are using a custom LR scheduler` if self.use_cyclic_lr: print(f"Using cyclic learning rate. min_lr: {self.learning_rate[0]}, max_lr: {self.learning_rate[1]}") lr_scheduler = torch.optim.lr...
deftraining_step_end(self,outputs):train_acc=self.train_acc(outputs['preds'],outputs['y']).item()self.log("train_acc",train_acc,prog_bar=True)return{"loss":outputs["loss"].mean()}#定义optimizer,以及可选的lr_scheduler defconfigure_optimizers(self):returntorch.optim.Adam(self.parameters(...
(self):generator_opt=Adam(self.model_gen.parameters(),lr=0.01)disriminator_opt=Adam(self.model_disc.parameters(),lr=0.02)discriminator_sched=CosineAnnealing(discriminator_opt,T_max=10)return[generator_opt,disriminator_opt],[discriminator_sched]# examplewithstep-based learning rate schedulers...