66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
import os
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import pickle
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import lmdb
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from torch.utils.data import Dataset
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from tqdm import tqdm
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from data.transform import transform_pipeline
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def default_transform_way(transform, sample):
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return [transform(sample[0]), *sample[1:]]
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class LMDBDataset(Dataset):
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def __init__(self, lmdb_path, pipeline=None, transform_way=default_transform_way, map_size=2 ** 40, readonly=True,
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**lmdb_kwargs):
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self.path = lmdb_path
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self.db = lmdb.open(lmdb_path, subdir=os.path.isdir(lmdb_path), map_size=map_size, readonly=readonly,
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lock=False, **lmdb_kwargs)
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with self.db.begin(write=False) as txn:
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self._len = pickle.loads(txn.get(b"$$len$$"))
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self.done_pipeline = pickle.loads(txn.get(b"$$done_pipeline$$"))
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if pipeline is None:
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self.not_done_pipeline = []
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else:
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self.not_done_pipeline = self._remain_pipeline(pipeline)
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self.transform = transform_pipeline(self.not_done_pipeline)
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self.transform_way = transform_way
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essential_attr = pickle.loads(txn.get(b"$$essential_attr$$"))
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for ea in essential_attr:
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setattr(self, ea, pickle.loads(txn.get(f"${ea}$".encode(encoding="utf-8"))))
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def _remain_pipeline(self, pipeline):
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for i, dp in enumerate(self.done_pipeline):
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if pipeline[i] != dp:
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raise ValueError(
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f"pipeline {self.done_pipeline} saved in this lmdb database is not match with pipeline:{pipeline}")
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return pipeline[len(self.done_pipeline):]
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def __repr__(self):
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return f"LMDBDataset: {self.path}\nlength: {len(self)}\n{self.transform}"
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def __len__(self):
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return self._len
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def __getitem__(self, idx):
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with self.db.begin(write=False) as txn:
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sample = pickle.loads(txn.get("{}".format(idx).encode()))
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sample = self.transform_way(self.transform, sample)
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return sample
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@staticmethod
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def lmdbify(dataset, done_pipeline, lmdb_path):
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env = lmdb.open(lmdb_path, map_size=2 ** 40, subdir=os.path.isdir(lmdb_path))
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with env.begin(write=True) as txn:
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for i in tqdm(range(len(dataset)), ncols=0):
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txn.put("{}".format(i).encode(), pickle.dumps(dataset[i]))
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txn.put(b"$$len$$", pickle.dumps(len(dataset)))
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txn.put(b"$$done_pipeline$$", pickle.dumps(done_pipeline))
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essential_attr = getattr(dataset, "essential_attr", list())
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txn.put(b"$$essential_attr$$", pickle.dumps(essential_attr))
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for ea in essential_attr:
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txn.put(f"${ea}$".encode(encoding="utf-8"), pickle.dumps(getattr(dataset, ea)))
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