utils.py

  
import torch
import shutil
import os
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import copy


def shrink_dataset(dataset, idx_list):
    dataset.data = dataset.data[idx_list]
    targets_numpy = np.array(dataset.targets)
    dataset.targets = targets_numpy[idx_list].tolist()
    dataset.idx_list = idx_list
    return dataset


def split_dataset(dataset, seed):
    dataset1 = dataset
    dataset2 = copy.deepcopy(dataset)
    np.random.seed(seed)
    total_num = len(dataset)
    idx_list = np.random.permutation(total_num)
    idx_list1 = idx_list[:total_num//2]
    idx_list2 = idx_list[total_num//2:]
    dataset1 = shrink_dataset(dataset1, idx_list1)
    dataset2 = shrink_dataset(dataset2, idx_list2)
    return dataset1, dataset2


def prepare_folders(args):
    """Create log and model folder"""
    folders_util = [args.root_log, args.root_model,
                    os.path.join(args.root_log, args.store_name),
                    os.path.join(args.root_model, args.store_name)]
    for folder in folders_util:
        if not os.path.exists(folder):
            print('creating folder ' + folder)
            os.mkdir(folder)


def save_checkpoint(args, state, split):
    filename = '%s/%s/ckpt.pth.tar%d' % (args.root_model, args.store_name, split)
    torch.save(state, filename)


class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        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

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

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

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