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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 | |
| ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | |
| import math | |
| 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 = %.7f, \ | |
| previous best = %.4f' % (epoch+1, 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 | |
| for i in range(len(optimizer.param_groups)): | |
| if optimizer.param_groups[i]['lr'] > 0: optimizer.param_groups[i]['lr'] = lr | |
| # optimizer.param_groups[0]['lr'] = lr | |
| # for i in range(1, len(optimizer.param_groups)): | |
| # optimizer.param_groups[i]['lr'] = lr * 10 | |