Source code for encoding.utils.lr_scheduler

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## 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
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import math

[docs]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): self.mode = mode print('Using {} LR Scheduler!'.format(self.mode)) self.lr = base_lr if mode == 'step': assert lr_step self.lr_step = lr_step self.iters_per_epoch = iters_per_epoch self.N = num_epochs * iters_per_epoch self.epoch = -1 self.warmup_iters = warmup_epochs * iters_per_epoch def __call__(self, optimizer, i, epoch, best_pred): T = epoch * self.iters_per_epoch + i if self.mode == 'cos': lr = 0.5 * self.lr * (1 + math.cos(1.0 * T / self.N * math.pi)) elif self.mode == 'poly': lr = self.lr * pow((1 - 1.0 * T / self.N), 0.9) elif self.mode == 'step': lr = self.lr * (0.1 ** (epoch // self.lr_step)) else: raise NotImplemented # warm up lr schedule if self.warmup_iters > 0 and T < self.warmup_iters: lr = lr * 1.0 * T / self.warmup_iters if epoch > self.epoch: print('\n=>Epoches %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): 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