utils.py

""" Utilities """
import os
import logging
import shutil
import torch
import torchvision.datasets as dset
import numpy as np
import preproc
from torch.utils.data import Dataset


def get_data(config, dataset, data_path, cutout_length, validation):
    """ Get torchvision dataset """
    dataset = dataset.lower()

    if dataset == 'cifar10':
        dset_cls = dset.CIFAR10
        n_classes = 10
    elif dataset == 'cifar100':
        dset_cls = dset.CIFAR100
        n_classes = 100
    elif dataset == 'mnist':
        dset_cls = dset.MNIST
        n_classes = 10
    elif dataset == 'fashionmnist':
        dset_cls = dset.FashionMNIST
        n_classes = 10
    elif dataset == 'tiny_imagenet':
        sub_dir = config.tiny_imagenet_path
        n_classes = 200
    elif dataset == 'imagenet':
        sub_dir_train = config.imagenet_train_path
        sub_dir_valid = config.imagenet_valid_path
        n_classes = 1000
    else:
        raise ValueError(dataset)

    if dataset == 'tiny_imagenet':
        trn_transform, val_transform = preproc.data_transforms(dataset, cutout_length)
        trn_data = dset.ImageFolder(os.path.join(sub_dir, 'train'), trn_transform)
        # trn_data = dset_cls(root=data_path, train=True, download=True, transform=trn_transform)
    elif dataset == 'imagenet':
        trn_transform, val_transform = preproc.data_transforms(dataset, cutout_length)
        trn_data = dset.ImageFolder(os.path.join(sub_dir_train, 'train'), trn_transform)
    else:
        trn_transform, val_transform = preproc.data_transforms(dataset, cutout_length)
        trn_data = dset_cls(root=data_path, train=True, download=True, transform=trn_transform)

    # assuming shape is NHW or NHWC
    # shape = trn_data.train_data.shape
    if dataset == 'tiny_imagenet':
        shape = (1, 3, 64, 64)
        input_channels = 3
        ret = [64, 3, 200, trn_data]
    elif dataset == 'imagenet':
        shape = (1, 3, 224, 224)
        input_channels = 3
        ret = [224, 3, 1000, trn_data]
    else:
        shape = trn_data.data.shape
        input_channels = 3 if len(shape) == 4 else 1
        assert shape[1] == shape[2], "not expected shape = {}".format(shape)
        input_size = shape[1]

        ret = [input_size, input_channels, n_classes, trn_data]
    if validation: # append validation data
        if dataset == 'tiny_imagenet':
            ret.append(dset.ImageFolder(os.path.join(sub_dir, 'val'), val_transform))
        elif dataset == 'imagenet':
            ret.append(dset.ImageFolder(os.path.join(sub_dir_valid, 'val'), val_transform))
        else:
            ret.append(dset_cls(root=data_path, train=False, download=True, transform=val_transform))
    return ret


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

    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    # one-hot case
    if target.ndimension() > 1:
        target = target.max(1)[1]

    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_(1.0 / batch_size))

    return res



def save_checkpoint_imagenet(config, state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, os.path.join(config.path, filename))
    if is_best:
        shutil.copyfile(os.path.join(config.path, filename), os.path.join(config.path, 'model_best.pth.tar'))
    
    
def save_checkpoint(state, ckpt_dir, is_best=False):
    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)