mmpretrain/mmcls/datasets/cifar.py

# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path
import pickle

import numpy as np
import torch.distributed as dist
from mmcv.runner import get_dist_info

from .base_dataset import BaseDataset
from .builder import DATASETS
from .utils import check_integrity, download_and_extract_archive


@DATASETS.register_module()
class CIFAR10(BaseDataset):
    """`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.

    This implementation is modified from
    https://github.com/pytorch/vision/blob/master/torchvision/datasets/cifar.py
    """  # noqa: E501

    base_folder = 'cifar-10-batches-py'
    url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
    filename = 'cifar-10-python.tar.gz'
    tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
    train_list = [
        ['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
        ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
        ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
        ['data_batch_4', '634d18415352ddfa80567beed471001a'],
        ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
    ]

    test_list = [
        ['test_batch', '40351d587109b95175f43aff81a1287e'],
    ]
    meta = {
        'filename': 'batches.meta',
        'key': 'label_names',
        'md5': '5ff9c542aee3614f3951f8cda6e48888',
    }
    CLASSES = [
        'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog',
        'horse', 'ship', 'truck'
    ]

    def load_annotations(self):

        rank, world_size = get_dist_info()

        if rank == 0 and not self._check_integrity():
            download_and_extract_archive(
                self.url,
                self.data_prefix,
                filename=self.filename,
                md5=self.tgz_md5)

        if world_size > 1:
            dist.barrier()
            assert self._check_integrity(), \
                'Shared storage seems unavailable. ' \
                f'Please download the dataset manually through {self.url}.'

        if not self.test_mode:
            downloaded_list = self.train_list
        else:
            downloaded_list = self.test_list

        self.imgs = []
        self.gt_labels = []

        # load the picked numpy arrays
        for file_name, checksum in downloaded_list:
            file_path = os.path.join(self.data_prefix, self.base_folder,
                                     file_name)
            with open(file_path, 'rb') as f:
                entry = pickle.load(f, encoding='latin1')
                self.imgs.append(entry['data'])
                if 'labels' in entry:
                    self.gt_labels.extend(entry['labels'])
                else:
                    self.gt_labels.extend(entry['fine_labels'])

        self.imgs = np.vstack(self.imgs).reshape(-1, 3, 32, 32)
        self.imgs = self.imgs.transpose((0, 2, 3, 1))  # convert to HWC

        self._load_meta()

        data_infos = []
        for img, gt_label in zip(self.imgs, self.gt_labels):
            gt_label = np.array(gt_label, dtype=np.int64)
            info = {'img': img, 'gt_label': gt_label}
            data_infos.append(info)
        return data_infos

    def _load_meta(self):
        path = os.path.join(self.data_prefix, self.base_folder,
                            self.meta['filename'])
        if not check_integrity(path, self.meta['md5']):
            raise RuntimeError(
                'Dataset metadata file not found or corrupted.' +
                ' You can use download=True to download it')
        with open(path, 'rb') as infile:
            data = pickle.load(infile, encoding='latin1')
            self.CLASSES = data[self.meta['key']]

    def _check_integrity(self):
        root = self.data_prefix
        for fentry in (self.train_list + self.test_list):
            filename, md5 = fentry[0], fentry[1]
            fpath = os.path.join(root, self.base_folder, filename)
            if not check_integrity(fpath, md5):
                return False
        return True


@DATASETS.register_module()
class CIFAR100(CIFAR10):
    """`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset."""

    base_folder = 'cifar-100-python'
    url = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
    filename = 'cifar-100-python.tar.gz'
    tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
    train_list = [
        ['train', '16019d7e3df5f24257cddd939b257f8d'],
    ]

    test_list = [
        ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
    ]
    meta = {
        'filename': 'meta',
        'key': 'fine_label_names',
        'md5': '7973b15100ade9c7d40fb424638fde48',
    }
    CLASSES = [
        'apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee',
        'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus',
        'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle',
        'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'crab',
        'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish',
        'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'keyboard',
        'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', 'man',
        'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom',
        'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear',
        'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine',
        'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea',
        'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake',
        'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper',
        'table', 'tank', 'telephone', 'television', 'tiger', 'tractor',
        'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale',
        'willow_tree', 'wolf', 'woman', 'worm'
    ]