LR keeps growing after using CyclicLR scheduler.

[Minxiangliu]

I want to add CyclicLR during training. I refer to this docs and the monai tutorials, but I see in the log that LR is not changing with Cyclic, but increasing continuously. What is wrong with my code?

Code:

class Net(pytorch_lightning.LightningModule):
    def __init__(self, *args, **kwargs):
        super().__init__()
   .....

    def prepare_data(self):
        self.train_ds, self.test_ds = getDataSet(self.stage)

    def train_dataloader(self):
        return ThreadDataLoader(self.train_ds, 
                                num_workers=0, 
                                batch_size=self.batch_size, 
                                shuffle=True,
                                drop_last=True)

    def configure_optimizers(self):
        steepest_lr = 0.005
        optimizer = torch.optim.Adam(self.model.parameters(), lr = steepest_lr*0.5)
        self.scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer=optimizer,
                                                           base_lr=steepest_lr*0.5,
                                                           max_lr=steepest_lr*1.5,
                                                           step_size_up=len(self.train_dataloader())*8,
                                                           cycle_momentum=False)
        return [optimizer], [self.scheduler]

    def training_step(self, batch, batch_idx):
        .....
        self.log('LR', {'learning_rate' : self.scheduler.get_last_lr()[0]}, on_epoch=False)
        return ...

Log: