Algorithm-Repo/SourceCode/MachineLearningUtils/Classification/PytorchFramework/LR_Scheduler.py

##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## ECE Department, Rutgers University
## Email: zhang.hang@rutgers.edu
## Copyright (c) 2017
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

import math

__all__ = ['LR_Scheduler', 'LR_Scheduler_Head']

class LR_Scheduler(object):
    """Learning Rate Scheduler

    Step mode: ``lr = baselr * 0.1 ^ {floor(epoch-1 / lr_step)}``

    Cosine mode: ``lr = baselr * 0.5 * (1 + cos(iter/maxiter))``

    Poly mode: ``lr = baselr * (1 - iter/maxiter) ^ 0.9``

    Args:
        args:  :attr:`args.lr_scheduler` lr scheduler mode (`cos`, `poly`),
          :attr:`args.lr` base learning rate, :attr:`args.epochs` number of epochs,
          :attr:`args.lr_step`

        iters_per_epoch: number of iterations per epoch
    """
    def __init__(self, mode, base_lr, num_epochs, iters_per_epoch=0,
                 lr_step=0, warmup_epochs=0, quiet=False):
        self.mode = mode
        self.quiet = quiet
        if not quiet:
            print('Using {} LR scheduler with warm-up epochs of {}!'.format(self.mode, warmup_epochs))
        if mode == 'step':
            assert lr_step
        self.base_lr = base_lr
        self.lr_step = lr_step
        self.iters_per_epoch = iters_per_epoch
        self.epoch = -1
        self.warmup_iters = warmup_epochs * iters_per_epoch
        self.total_iters = (num_epochs - warmup_epochs) * iters_per_epoch

    def __call__(self, optimizer, i, epoch, best_pred):
        T = epoch * self.iters_per_epoch + i
        # warm up lr schedule
        if self.warmup_iters > 0 and T < self.warmup_iters:
            lr = self.base_lr * 1.0 * T / self.warmup_iters
        elif self.mode == 'cos':
            T = T - self.warmup_iters
            lr = 0.5 * self.base_lr * (1 + math.cos(1.0 * T / self.total_iters * math.pi))
        elif self.mode == 'poly':
            T = T - self.warmup_iters
            lr = self.base_lr * pow((1 - 1.0 * T / self.total_iters), 0.9)
        elif self.mode == 'step':
            lr = self.base_lr * (0.1 ** (epoch // self.lr_step))
        else:
            raise NotImplemented
        if epoch > self.epoch and (epoch == 0 or best_pred > 0.0):
            if not self.quiet:
                print('\n=>Epoch %i, learning rate = %.4f, \
                    previous best = %.4f' % (epoch, lr, best_pred))
            self.epoch = epoch
        assert lr >= 0
        self._adjust_learning_rate(optimizer, lr)

    def _adjust_learning_rate(self, optimizer, lr):
        for i in range(len(optimizer.param_groups)):
            optimizer.param_groups[i]['lr'] = lr

class LR_Scheduler_Head(LR_Scheduler):
    """Incease the additional head LR to be 10 times"""
    def _adjust_learning_rate(self, optimizer, lr):
        if len(optimizer.param_groups) == 1:
            optimizer.param_groups[0]['lr'] = lr
        else:
            # enlarge the lr at the head
            optimizer.param_groups[0]['lr'] = lr
            for i in range(1, len(optimizer.param_groups)):
                optimizer.param_groups[i]['lr'] = lr * 10