Source code for pywick.callbacks.CyclicLRScheduler

# Source: (MIT)
# Good description of how it functions is here:

# This code is from
# This code is under review at PyTorch and is to be merged eventually to make CLR available to all.
# Tested with pytorch 0.2.0

from torch.optim.optimizer import Optimizer
import numpy as np
from . import Callback
from ..misc import trun_n_d

widegap_scale_fn = lambda x: 1/(5**(x*0.0001))

[docs]class CyclicLRScheduler(Callback): """Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). The policy cycles the learning rate between two boundaries with a constant frequency, as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. The distance between the two boundaries can be scaled on a per-iteration or per-cycle basis. Cyclical learning rate policy changes the learning rate after every batch. `batch_step` should be called after a batch has been used for training. To resume training, save `last_batch_iteration` and use it to instantiate `CycleLR`. This class has three built-in policies, as put forth in the paper: "triangular": A basic triangular cycle w/ no amplitude scaling. "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. "exp_range": A cycle that scales initial amplitude by gamma**(cycle iterations) at each cycle iteration. This implementation was adapted from the github repo: `bckenstler/CLR`_ :param optimizer: (Optimizer): Wrapped optimizer. :param base_lr: (float or list): Initial learning rate which is the lower boundary in the cycle for eachparam groups. Default: 0.001 :param max_lr: (float or list): Upper boundaries in the cycle for each parameter group. Functionally, it defines the cycle amplitude (max_lr - base_lr). The lr at any cycle is the sum of base_lr and some scaling of the amplitude; therefore max_lr may not actually be reached depending on scaling function. Default: 0.006 :param step_size: (int): Number of training iterations per half cycle. Authors suggest setting step_size 2-8 x training iterations in epoch. Default: 2000 :param mode: (str): One of {triangular, triangular2, exp_range}. Values correspond to policies detailed above. If scale_fn is not None, this argument is ignored. Default: 'triangular' :param gamma: (float): Constant in 'exp_range' scaling function: gamma**(cycle iterations) Default: 1.0 :param scale_fn: (function): Custom scaling policy defined by a single argument lambda function, where 0 <= scale_fn(x) <= 1 for all x >= 0. mode paramater is ignored Default: None :param scale_mode: (str): {'cycle', 'iterations'}. Defines whether scale_fn is evaluated on cycle number or cycle iterations (training iterations since start of cycle). Default: 'cycle' :param verbose: (bool): Whether to produce some output during initialization Default: True Example: >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) >>> scheduler = torch.optim.CyclicLR(optimizer) >>> data_loader = >>> for epoch in range(10): >>> for batch in data_loader: >>> scheduler.batch_step() >>> train_batch(...) .. _Cyclical Learning Rates for Training Neural Networks: .. _bckenstler/CLR: """ def __init__(self, optimizer, base_lr=1e-3, max_lr=6e-3, step_size=2000, mode='triangular', gamma=1., scale_fn=None, scale_mode='cycle', verbose=True): if not isinstance(optimizer, Optimizer): raise TypeError('{} is not an Optimizer'.format(type(optimizer).__name__)) self.optimizer = optimizer if isinstance(base_lr, (list, tuple)): if len(base_lr) != len(optimizer.param_groups): raise ValueError("expected {} base_lr, got {}".format(len(optimizer.param_groups), len(base_lr))) self.base_lrs = list(base_lr) else: self.base_lrs = [base_lr] * len(optimizer.param_groups) if isinstance(max_lr, (list, tuple)): if len(max_lr) != len(optimizer.param_groups): raise ValueError("expected {} max_lr, got {}".format(len(optimizer.param_groups), len(max_lr))) self.max_lrs = list(max_lr) else: self.max_lrs = [max_lr] * len(optimizer.param_groups) self.step_size = step_size if verbose: print('CyclicLRScheduler params:') print('\tstep_size: {}'.format(step_size)) print('\tmode: {}'.format(mode)) print('\tbase_lr: {}'.format(base_lr)) print('\tmax_lr: {}'.format(max_lr)) if mode not in ['triangular', 'triangular2', 'exp_range'] and scale_fn is None: raise ValueError('mode is invalid and scale_fn is None') self.mode = mode self.gamma = gamma if scale_fn is None: if self.mode == 'triangular': self.scale_fn = self._triangular_scale_fn self.scale_mode = 'cycle' elif self.mode == 'triangular2': self.scale_fn = self._triangular2_scale_fn self.scale_mode = 'cycle' elif self.mode == 'exp_range': self.scale_fn = self._exp_range_scale_fn self.scale_mode = 'iterations' else: self.scale_fn = scale_fn self.scale_mode = scale_mode self.last_batch_iteration = 0 self.epoch_count = 0 self.optimizer_name = optimizer.__class__.__name__.lower() def on_batch_end(self, batch, logs=None): if 'yellowfin' in self.optimizer_name: computed_lr = [self.optimizer._optimizer.param_groups[0]['lr']] # this is because trainer history expects a list else: computed_lr = self.get_lr() # returns a list self.last_batch_iteration = self.last_batch_iteration + 1 # global iteration counter for param_group, lr in zip(self.optimizer.param_groups, computed_lr): param_group['lr'] = lr if self.trainer.history is not None: for i,lr in enumerate(computed_lr): computed_lr[i] = trun_n_d(lr.item(), 5) # .item() is a numpy way of obtaining a float self.trainer.history.lrs = computed_lr @staticmethod def _triangular_scale_fn(x): return 1. @staticmethod def _triangular2_scale_fn(x): return 1 / (2. ** (x - 1)) def _exp_range_scale_fn(self, x): return self.gamma**(x) def get_lr(self): step_size = float(self.step_size) cycle = np.floor(1 + self.last_batch_iteration / (2 * step_size)) # cycle number is based on global batch counter x = np.abs(self.last_batch_iteration / step_size - 2 * cycle + 1) lrs = [] param_lrs = zip(self.optimizer.param_groups, self.base_lrs, self.max_lrs) for param_group, base_lr, max_lr in param_lrs: base_height = (max_lr - base_lr) * np.maximum(0, (1 - x)) if self.scale_mode == 'cycle': lr = base_lr + base_height * self.scale_fn(cycle) else: lr = base_lr + base_height * self.scale_fn(self.last_batch_iteration) lrs.append(lr) return lrs