utils.py

import torch
import logging


def setup_logging(log_file='log.txt'):
    """Setup logging configuration
    """
    logging.basicConfig(level=logging.DEBUG,
                        format="%(asctime)s - %(levelname)s - %(message)s",
                        datefmt="%Y-%m-%d %H:%M:%S",
                        filename=log_file,
                        filemode='w')
    console = logging.StreamHandler()
    console.setLevel(logging.INFO)
    formatter = logging.Formatter('%(message)s')
    console.setFormatter(formatter)
    logging.getLogger('').addHandler(console)


__optimizers = {
    'SGD': torch.optim.SGD,
    'ASGD': torch.optim.ASGD,
    'Adam': torch.optim.Adam,
    'Adamax': torch.optim.Adamax,
    'Adagrad': torch.optim.Adagrad,
    'Adadelta': torch.optim.Adadelta,
    'Rprop': torch.optim.Rprop,
    'RMSprop': torch.optim.RMSprop
}


def select_optimizer(optimizer_name, params, *kargs, **kwargs):
    return __optimizers[optimizer_name](params, kargs, kwargs)


def adjust_optimizer(optimizer, epoch, config):
    """Reconfigures the optimizer according to epoch and config dict"""
    def modify_optimizer(optimizer, setting):
        for param_group in optimizer.param_groups:
            for key in param_group.keys():
                if key in setting:
                    logging.debug('OPTIMIZER - setting %s = %s' %
                                  (key, setting[key]))
                    param_group[key] = setting[key]
        return optimizer

    if callable(config):
        optimizer = modify_optimizer(optimizer, config(epoch))
    else:
        for e in range(epoch):  # run over all epochs - sticky setting
            if e in config:
                optimizer = modify_optimizer(optimizer, config[e])

    return optimizer


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count