models/optimizer.py

from torch.optim import lr_scheduler
import torch
from .nero import Nero
# import torch_optimizer as optim
import ast
from madgrad import MADGRAD
from pl_bolts.optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR


def get_optimizers(net, opt):
    if opt.optimizer == "adam":
        optimizer = torch.optim.Adam(net.parameters(), lr=opt.learning_rate, weight_decay=opt.weight_decay)
    elif opt.optimizer == "adamw":
        optimizer = torch.optim.AdamW(net.parameters(), lr=opt.learning_rate, weight_decay=opt.weight_decay, eps=1e-05, betas=(0.9, 0.95))
    elif opt.optimizer == "sgd":
        optimizer = torch.optim.SGD(net.parameters(), lr=opt.learning_rate, momentum=opt.momentum, weight_decay=opt.weight_decay)
    elif opt.optimizer == "adagrad":
        optimizer = torch.optim.Adagrad(net.parameters(), lr=opt.learning_rate, weight_decay=opt.weight_decay)
    elif opt.optimizer == "adadelta":
        optimizer = torch.optim.Adadelta(net.parameters(), lr=opt.learning_rate, weight_decay=opt.weight_decay)
    elif opt.optimizer == "rmsprop":
        optimizer = torch.optim.Rmsprop(net.parameters(), lr=opt.learning_rate, weight_decay=opt.weight_decay)
    elif opt.optimizer == "nero":
        optimizer = Nero(net.parameters(), lr=opt.learning_rate)
    elif opt.optimizer == "madgrad":
        optimizer = MADGRAD(net.parameters(), lr=opt.learning_rate, weight_decay=opt.weight_decay, momentum=opt.momentum)
    # elif opt.optimizer == "ranger":
    #     optimizer = optim.Ranger(net.parameters(), lr=opt.learning_rate, alpha=0.5, k=6, N_sma_threshhold=5, betas=(.95, 0.999), eps=1e-5, weight_decay=0 )
    else:
        return NotImplementedError('optimizer [%s] is not implemented', opt.optimizer)
    return [optimizer]

def get_scheduler(optimizer, opt):
    if opt.lr_policy == 'lambda':
        def lambda_rule(epoch):
            nepochs = opt.max_epochs - opt.nepoch_decay #number of epochs before beginning to decay
            lr_l = 1.0 - max(0, epoch + opt.epoch_count - nepochs) / float(opt.nepoch_decay + 1)
            return lr_l
        scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
    elif opt.lr_policy == 'exponential':
        scheduler = lr_scheduler.ExponentialLR(optimizer = optimizer, gamma = opt.lr_decay_factor)
    elif opt.lr_policy == 'step':
        scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=opt.lr_decay_factor)
    elif opt.lr_policy == 'multistep':
        scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=ast.literal_eval(opt.lr_decay_milestones), gamma=opt.lr_decay_factor)
    elif opt.lr_policy == 'plateau':
        scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
    elif opt.lr_policy == 'cosine':
        scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.nepoch, eta_min=0)
    elif opt.lr_policy == 'cyclic':
        scheduler = CyclicLR(optimizer, base_lr=opt.learning_rate / 10, max_lr=opt.learning_rate,
                             step_size=opt.nepoch_decay, mode='triangular2')
    elif opt.lr_policy == 'reduceOnPlateau':
        scheduler = ReduceLROnPlateau(optimizer, 'min', factor=0.2)
    elif opt.lr_policy == 'LinearWarmupCosineAnnealing':
        scheduler = LinearWarmupCosineAnnealingLR(optimizer, warmup_epochs=opt.warmup_epochs, max_epochs=opt.max_epochs)
    else:
        return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
    return scheduler


class CyclicLR(object):
    """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`_

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        base_lr (float or list): Initial learning rate which is the
            lower boundary in the cycle for eachparam groups.
            Default: 0.001
        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
        step_size (int): Number of training iterations per
            half cycle. Authors suggest setting step_size
            2-8 x training iterations in epoch. Default: 2000
        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'
        gamma (float): Constant in 'exp_range' scaling function:
            gamma**(cycle iterations)
            Default: 1.0
        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
        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'
        last_batch_iteration (int): The index of the last batch. Default: -1

    Example:
        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
        >>> scheduler = torch.optim.CyclicLR(optimizer)
        >>> data_loader = torch.utils.data.DataLoader(...)
        >>> for epoch in range(10):
        >>>     for batch in data_loader:
        >>>         scheduler.batch_step()
        >>>         train_batch(...)

    .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186
    .. _bckenstler/CLR: https://github.com/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', last_batch_iteration=-1):

        if not isinstance(optimizer, Optimizer):
            raise TypeError('{} is not an Optimizer'.format(
                type(optimizer).__name__))
        self.optimizer = optimizer

        if isinstance(base_lr, list) or isinstance(base_lr, 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) or isinstance(max_lr, 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 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.batch_step(last_batch_iteration + 1)
        self.last_batch_iteration = last_batch_iteration

    def batch_step(self, batch_iteration=None):
        if batch_iteration is None:
            batch_iteration = self.last_batch_iteration + 1
        self.last_batch_iteration = batch_iteration
        for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
            param_group['lr'] = lr

    def _triangular_scale_fn(self, x):
        return 1.

    def _triangular2_scale_fn(self, 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))
        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