utils.py

"""
Utilities
"""

import os
import logging
import shutil
import numpy as np

import torch
import torch.nn as nn


def encrypt_with_iso(g, ratio, th=5.0, device='cpu'):
    g = g.cpu()

    g_original_shape = g.shape
    g = g.view(g_original_shape[0], -1)

    g_norm = torch.norm(g, dim=1, keepdim=False)
    g_norm = g_norm.view(-1, 1)
    max_norm = torch.max(g_norm)
    gaussian_noise = torch.normal(size=g.shape, mean=0.0,
                                  std=1e-6+ratio * max_norm / torch.sqrt(torch.tensor(g.shape[1], dtype=torch.float32)))
    res = g + gaussian_noise
    res = res.view(g_original_shape).to(device)

    return res


def get_logger(file_path):
    """ Make python logger """
    # [!] Since tensorboardX use default logger (e.g. logging.info()), we should use custom logger
    logger = logging.getLogger('darts')
    log_format = '%(asctime)s | %(message)s'
    formatter = logging.Formatter(log_format, datefmt='%m/%d %I:%M:%S %p')
    file_handler = logging.FileHandler(file_path, mode='w')
    file_handler.setFormatter(formatter)
    stream_handler = logging.StreamHandler()
    stream_handler.setFormatter(formatter)

    logger.addHandler(file_handler)
    logger.addHandler(stream_handler)
    logger.setLevel(logging.INFO)

    return logger


def param_size(model):
    """ Compute parameter size in MB """
    n_params = sum(
        np.prod(v.size()) for k, v in model.named_parameters() if not k.startswith('aux_head'))
    return n_params / 1024. / 1024.


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

    def __init__(self):
        self.reset()

    def reset(self):
        """ Reset all statistics """
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

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


def accuracy(output, target, topk=(1,)):
    maxk = max(topk)
    batch_size = target.size(0)

    _, pred = output.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].reshape(-1).float().sum(0)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res


def create_exp_dir(path, scripts_to_save=None):
    os.makedirs(path, exist_ok=True)
    print('Experiment dir : {}'.format(path))

    if scripts_to_save is not None:
        os.makedirs(os.path.join(path, 'scripts'), exist_ok=True)
        for script in scripts_to_save:
            dst_file = os.path.join(path, 'scripts', os.path.basename(script))
            shutil.copyfile(script, dst_file)


def save_checkpoint(state, ckpt_dir, is_best=False):
    os.makedirs(ckpt_dir, exist_ok=True)
    filename = os.path.join(ckpt_dir, 'checkpoint.pth.tar')
    torch.save(state, filename)
    if is_best:
        best_filename = os.path.join(ckpt_dir, 'best.pth.tar')
        shutil.copyfile(filename, best_filename)