MQ-Det/maskrcnn_benchmark/solver/lr_scheduler.py

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from bisect import bisect_right

import math
import torch


# FIXME ideally this would be achieved with a CombinedLRScheduler,
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
    def __init__(
        self,
        optimizer,
        milestones,
        gamma=0.1,
        warmup_factor=1.0 / 3,
        warmup_iters=500,
        warmup_method="linear",
        last_epoch=-1,
    ):
        if not list(milestones) == sorted(milestones):
            raise ValueError(
                "Milestones should be a list of" " increasing integers. Got {}",
                milestones,
            )

        if warmup_method not in ("constant", "linear"):
            raise ValueError(
                "Only 'constant' or 'linear' warmup_method accepted"
                "got {}".format(warmup_method)
            )
        self.milestones = milestones
        self.gamma = gamma
        self.warmup_factor = warmup_factor
        self.warmup_iters = warmup_iters
        self.warmup_method = warmup_method
        super(WarmupMultiStepLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        warmup_factor = 1
        if self.last_epoch < self.warmup_iters:
            if self.warmup_method == "constant":
                warmup_factor = self.warmup_factor
            elif self.warmup_method == "linear":
                alpha = float(self.last_epoch) / self.warmup_iters
                warmup_factor = self.warmup_factor * (1 - alpha) + alpha
        return [
            base_lr
            * warmup_factor
            * self.gamma ** bisect_right(self.milestones, self.last_epoch)
            for base_lr in self.base_lrs
        ]


class WarmupCosineAnnealingLR(torch.optim.lr_scheduler._LRScheduler):
    def __init__(
            self,
            optimizer,
            max_iters,
            gamma=0.1,
            warmup_factor=1.0 / 3,
            warmup_iters=500,
            warmup_method="linear",
            eta_min = 0,
            last_epoch=-1,
    ):

        if warmup_method not in ("constant", "linear"):
            raise ValueError(
                "Only 'constant' or 'linear' warmup_method accepted"
                "got {}".format(warmup_method)
            )
        self.max_iters = max_iters
        self.gamma = gamma
        self.warmup_factor = warmup_factor
        self.warmup_iters = warmup_iters
        self.warmup_method = warmup_method
        self.eta_min = eta_min
        super(WarmupCosineAnnealingLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        warmup_factor = 1

        if self.last_epoch < self.warmup_iters:
            if self.warmup_method == "constant":
                warmup_factor = self.warmup_factor
            elif self.warmup_method == "linear":
                alpha = float(self.last_epoch) / self.warmup_iters
                warmup_factor = self.warmup_factor * (1 - alpha) + alpha
            return [
                base_lr
                * warmup_factor
                for base_lr in self.base_lrs
            ]
        else:
            return [
                self.eta_min
                + (base_lr - self.eta_min)
                * (1 + math.cos(math.pi * (self.last_epoch - self.warmup_iters) / self.max_iters)) / 2
                for base_lr in self.base_lrs
            ]

class WarmupReduceLROnPlateau(torch.optim.lr_scheduler.ReduceLROnPlateau):
    def __init__(
            self,
            optimizer,
            max_iters,
            gamma=0.1,
            warmup_factor=1.0 / 3,
            warmup_iters=500,
            warmup_method="linear",
            eta_min = 0,
            last_epoch=-1,
            patience = 5,
            verbose = False,
    ):    

        if warmup_method not in ("constant", "linear"):
            raise ValueError(
                "Only 'constant' or 'linear' warmup_method accepted"
                "got {}".format(warmup_method)
            )
        self.warmup_factor = warmup_factor
        self.warmup_iters = warmup_iters
        self.warmup_method = warmup_method
        self.eta_min = eta_min

        if last_epoch == -1:
            for group in optimizer.param_groups:
                group.setdefault('initial_lr', group['lr'])
        else:
            for i, group in enumerate(optimizer.param_groups):
                if 'initial_lr' not in group:
                    raise KeyError("param 'initial_lr' is not specified "
                                   "in param_groups[{}] when resuming an optimizer".format(i))
        self.base_lrs = list(map(lambda group: group['initial_lr'], optimizer.param_groups))
        super(WarmupReduceLROnPlateau, self).__init__(optimizer, factor=gamma, patience=patience, mode='max', min_lr=eta_min, verbose = verbose)

    def step(self, metrics=None):
        warmup_factor = 1

        if self.last_epoch < self.warmup_iters:
            if self.warmup_method == "constant":
                warmup_factor = self.warmup_factor
            elif self.warmup_method == "linear":
                alpha = float(self.last_epoch) / self.warmup_iters
                warmup_factor = self.warmup_factor * (1 - alpha) + alpha
            
            if self.last_epoch >= self.warmup_iters-1:
                warmup_factor = 1.0
                
            warmup_lrs = [
                base_lr
                * warmup_factor
                for base_lr in self.base_lrs
            ]

            for param_group, lr in zip(self.optimizer.param_groups, warmup_lrs):
                param_group['lr'] = lr
            
            self.last_epoch += 1
        elif metrics:
            super().step(metrics)