Pytorch Slanted Triangular Learning Rate Scheduler

stlr.py

class STLR(torch.optim.lr_scheduler._LRScheduler):
    def __init__(self, optimizer, max_mul, ratio, steps_per_cycle, decay=1, last_epoch=-1):
        self.max_mul = max_mul - 1
        self.turning_point = steps_per_cycle // (ratio + 1)
        self.steps_per_cycle = steps_per_cycle
        self.decay = decay
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        residual = self.last_epoch % self.steps_per_cycle
        multiplier = self.decay ** (self.last_epoch // self.steps_per_cycle)
        if residual <= self.turning_point:
            multiplier *= self.max_mul * (residual / self.turning_point)
        else:
            multiplier *= self.max_mul * (
                (self.steps_per_cycle - residual) /
                (self.steps_per_cycle - self.turning_point))
        return [lr * (1 + multiplier) for lr in self.base_lrs]

Can you explain what does mean all parameters and how do these match with original paper https://arxiv.org/pdf/1801.06146.pdf

You have to be more specific, i.e., specifying which part you don't understand. In most case, just try the optimizer and plot the learning rates should be enough for you to know how it works.