Source code for mmagic.datasets.cifar10_dataset
# Copyright (c) OpenMMLab. All rights reserved.
import pickle
from typing import List, Optional
import mmengine.dist as dist
import numpy as np
from mmengine.fileio import get_file_backend
from mmagic.registry import DATASETS
from .basic_conditional_dataset import BasicConditionalDataset
from .categories import CIFAR10_CATEGORIES
from .data_utils import check_md5, download_and_extract_archive
@DATASETS.register_module()
[docs]class CIFAR10(BasicConditionalDataset):
"""`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
Args:
data_prefix (str): Prefix for data.
test_mode (bool): ``test_mode=True`` means in test phase.
It determines to use the training set or test set.
metainfo (dict, optional): Meta information for dataset, such as
categories information. Defaults to None.
data_root (str): The root directory for ``data_prefix``.
Defaults to ''.
download (bool): Whether to download the dataset if not exists.
Defaults to True.
**kwargs: Other keyword arguments in :class:`BaseDataset`.
""" # noqa: E501
[docs] train_list = [
['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
['data_batch_4', '634d18415352ddfa80567beed471001a'],
['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
]
]
[docs] meta = {
'filename': 'batches.meta',
'key': 'label_names',
'md5': '5ff9c542aee3614f3951f8cda6e48888',
}
def __init__(self,
data_prefix: str,
test_mode: bool,
metainfo: Optional[dict] = None,
data_root: str = '',
download: bool = True,
**kwargs):
self.download = download
super().__init__(
# The CIFAR dataset doesn't need specify annotation file
ann_file='',
metainfo=metainfo,
data_root=data_root,
data_prefix=dict(root=data_prefix),
test_mode=test_mode,
**kwargs)
[docs] def load_data_list(self):
"""Load images and ground truth labels."""
root_prefix = self.data_prefix['root']
file_backend = get_file_backend(uri=root_prefix)
if dist.is_main_process() and not self._check_integrity():
if file_backend.name != 'LocalBackend':
raise RuntimeError(
f'The dataset on {root_prefix} is not integrated, '
f'please manually handle it.')
if self.download:
download_and_extract_archive(
self.url,
root_prefix,
filename=self.filename,
md5=self.tgz_md5)
else:
raise RuntimeError(
f'Cannot find {self.__class__.__name__} dataset in '
f"{self.data_prefix['root']}, you can specify "
'`download=True` to download automatically.')
dist.barrier()
assert self._check_integrity(), \
'Download failed or shared storage is unavailable. Please ' \
f'download the dataset manually through {self.url}.'
if not self.test_mode:
downloaded_list = self.train_list
else:
downloaded_list = self.test_list
imgs = []
gt_labels = []
# load the picked numpy arrays
for file_name, _ in downloaded_list:
file_path = file_backend.join_path(root_prefix, self.base_folder,
file_name)
content = file_backend.get(file_path)
entry = pickle.loads(content, encoding='latin1')
imgs.append(entry['data'])
if 'labels' in entry:
gt_labels.extend(entry['labels'])
else:
gt_labels.extend(entry['fine_labels'])
imgs = np.vstack(imgs).reshape(-1, 3, 32, 32)
imgs = imgs.transpose((0, 2, 3, 1)) # convert to HWC
if self.CLASSES is None:
# The metainfo in the file has the lowest priority, therefore
# we only need to load it if classes is not specified.
self._load_meta()
data_list = []
for img, gt_label in zip(imgs, gt_labels):
info = {
'gt': img,
'gt_label': int(gt_label),
'gt_channel_order': 'RGB'
}
data_list.append(info)
return data_list
[docs] def _load_meta(self):
"""Load categories information from metafile."""
root = self.data_prefix['root']
file_backend = get_file_backend(uri=root)
path = file_backend.join_path(root, self.base_folder,
self.meta['filename'])
md5 = self.meta.get('md5', None)
if not file_backend.exists(path) or (md5 is not None
and not check_md5(path, md5)):
raise RuntimeError(
'Dataset metadata file not found or corrupted.' +
' You can use `download=True` to download it')
content = file_backend.get(path)
data = pickle.loads(content, encoding='latin1')
self._metainfo.setdefault('classes', data[self.meta['key']])
[docs] def _check_integrity(self):
"""Check the integrity of data files."""
root = self.data_prefix['root']
file_backend = get_file_backend(uri=root)
for fentry in (self.train_list + self.test_list):
filename, md5 = fentry[0], fentry[1]
fpath = file_backend.join_path(root, self.base_folder, filename)
if not file_backend.exists(fpath):
return False
if md5 is not None and not check_md5(
fpath, md5, file_backend=file_backend):
return False
return True
[docs] def extra_repr(self) -> List[str]:
"""The extra repr information of the dataset."""
body = [f"Prefix of data: \t{self.data_prefix['root']}"]
return body