utils.py

import os
import torch
import torch.nn.functional as F
import logging.config
import shutil
import pandas as pd
from bokeh.io import output_file, save, show
from bokeh.plotting import figure
from bokeh.layouts import column
from bokeh.charts import Line, defaults
from numpy.random import choice

defaults.width = 800
defaults.height = 400
defaults.tools = 'pan,box_zoom,wheel_zoom,box_select,hover,resize,reset,save'


def setup_logging(log_file='log.txt'):
    """Setup logging configuration
    """
    logging.basicConfig(level=logging.INFO,
                        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)


class ResultsLog(object):

    def __init__(self, path='results.csv', plot_path=None):
        self.path = path
        self.plot_path = plot_path or (self.path + '.html')
        self.figures = []
        self.results = None

    def add(self, **kwargs):
        df = pd.DataFrame([kwargs.values()], columns=kwargs.keys())
        if self.results is None:
            self.results = df
        else:
            self.results = self.results.append(df, ignore_index=True)

    def save(self, title='Training Results'):
        if len(self.figures) > 0:
            if os.path.isfile(self.plot_path):
                os.remove(self.plot_path)
            output_file(self.plot_path, title=title)
            plot = column(*self.figures)
            save(plot)
            self.figures = []
        self.results.to_csv(self.path, index=False, index_label=False)

    def load(self, path=None):
        path = path or self.path
        if os.path.isfile(path):
            self.results.read_csv(path)

    def show(self):
        if len(self.figures) > 0:
            plot = column(*self.figures)
            show(plot)

    def plot(self, *kargs, **kwargs):
        line = Line(data=self.results, *kargs, **kwargs)
        self.figures.append(line)

    def image(self, *kargs, **kwargs):
        fig = figure()
        fig.image(*kargs, **kwargs)
        self.figures.append(fig)


def save_checkpoint(state, is_best, path='.', filename='checkpoint.pth.tar', save_all=False):
    filename = os.path.join(path, filename)
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, os.path.join(path, 'model_best.pth.tar'))
    if save_all:
        shutil.copyfile(filename, os.path.join(
            path, 'checkpoint_epoch_%s.pth.tar' % state['epoch']))


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


class OnlineMeasure(object):

    def __init__(self):
        self.mean = torch.FloatTensor(1).fill_(-1)
        self.M2 = torch.FloatTensor(1).zero_()
        self.count = 0.
        self.needs_init = True

    def reset(self, x):
        self.mean = x.new(x.size()).zero_()
        self.M2 = x.new(x.size()).zero_()
        self.count = 0.
        self.needs_init = False

    def update(self, x):
        self.val = x
        if self.needs_init:
            self.reset(x)
        self.count += 1
        delta = x - self.mean
        self.mean.add_(delta / self.count)
        delta2 = x - self.mean
        self.M2.add_(delta * delta2)

    def var(self):
        if self.count < 2:
            return self.M2.clone().zero_()
        return self.M2 / (self.count - 1)

    def std(self):
        return self.var().sqrt()

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

    if callable(config):
        optimizer = modify_optimizer(optimizer, config(epoch))
    else:
        for e in range(epoch + 1):  # run over all epochs - sticky setting
            if e in config:
                optimizer = modify_optimizer(optimizer, config[e])

    return optimizer


def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    batch_size = target.size(0)

    _, pred = output.float().topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.view(1, -1).expand_as(pred))

    res = []
    for k in topk:
        correct_k = correct[:k].view(-1).float().sum(0)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res



class RandomSamplerReplacment(torch.utils.data.sampler.Sampler):
    """Samples elements randomly, with replacement.
    Arguments:
        data_source (Dataset): dataset to sample from
    """

    def __init__(self, data_source):
        self.num_samples = len(data_source)

    def __iter__(self):
        return iter(torch.from_numpy(choice(self.num_samples, self.num_samples, replace=True)))

    def __len__(self):
        return self.num_samples