from .vision import VisionDataset from PIL import Image import os import os.path import io import string import sys if sys.version_info < (3, 3): from collections import Iterable else: from collections.abc import Iterable if sys.version_info[0] == 2: import cPickle as pickle else: import pickle from .utils import verify_str_arg, iterable_to_str class LSUNClass(VisionDataset): def __init__(self, root, transform=None, target_transform=None): import lmdb super(LSUNClass, self).__init__(root, transform=transform, target_transform=target_transform) self.env = lmdb.open(root, max_readers=1, readonly=True, lock=False, readahead=False, meminit=False) with self.env.begin(write=False) as txn: self.length = txn.stat()['entries'] cache_file = '_cache_' + ''.join(c for c in root if c in string.ascii_letters) if os.path.isfile(cache_file): self.keys = pickle.load(open(cache_file, "rb")) else: with self.env.begin(write=False) as txn: self.keys = [key for key, _ in txn.cursor()] pickle.dump(self.keys, open(cache_file, "wb")) def __getitem__(self, index): img, target = None, None env = self.env with env.begin(write=False) as txn: imgbuf = txn.get(self.keys[index]) buf = io.BytesIO() buf.write(imgbuf) buf.seek(0) img = Image.open(buf).convert('RGB') if self.transform is not None: img = self.transform(img) if self.target_transform is not None: target = self.target_transform(target) return img, target def __len__(self): return self.length class LSUN(VisionDataset): """ `LSUN `_ dataset. Args: root (string): Root directory for the database files. classes (string or list): One of {'train', 'val', 'test'} or a list of categories to load. e,g. ['bedroom_train', 'church_outdoor_train']. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. """ def __init__(self, root, classes='train', transform=None, target_transform=None): super(LSUN, self).__init__(root, transform=transform, target_transform=target_transform) self.classes = self._verify_classes(classes) # for each class, create an LSUNClassDataset self.dbs = [] for c in self.classes: self.dbs.append(LSUNClass( root=root + '/' + c + '_lmdb', transform=transform)) self.indices = [] count = 0 for db in self.dbs: count += len(db) self.indices.append(count) self.length = count def _verify_classes(self, classes): categories = ['bedroom', 'bridge', 'church_outdoor', 'classroom', 'conference_room', 'dining_room', 'kitchen', 'living_room', 'restaurant', 'tower'] dset_opts = ['train', 'val', 'test'] try: verify_str_arg(classes, "classes", dset_opts) if classes == 'test': classes = [classes] else: classes = [c + '_' + classes for c in categories] except ValueError: if not isinstance(classes, Iterable): msg = ("Expected type str or Iterable for argument classes, " "but got type {}.") raise ValueError(msg.format(type(classes))) classes = list(classes) msg_fmtstr = ("Expected type str for elements in argument classes, " "but got type {}.") for c in classes: verify_str_arg(c, custom_msg=msg_fmtstr.format(type(c))) c_short = c.split('_') category, dset_opt = '_'.join(c_short[:-1]), c_short[-1] msg_fmtstr = "Unknown value '{}' for {}. Valid values are {{{}}}." msg = msg_fmtstr.format(category, "LSUN class", iterable_to_str(categories)) verify_str_arg(category, valid_values=categories, custom_msg=msg) msg = msg_fmtstr.format(dset_opt, "postfix", iterable_to_str(dset_opts)) verify_str_arg(dset_opt, valid_values=dset_opts, custom_msg=msg) return classes def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: Tuple (image, target) where target is the index of the target category. """ target = 0 sub = 0 for ind in self.indices: if index < ind: break target += 1 sub = ind db = self.dbs[target] index = index - sub if self.target_transform is not None: target = self.target_transform(target) img, _ = db[index] return img, target def __len__(self): return self.length def extra_repr(self): return "Classes: {classes}".format(**self.__dict__)