74 lines
2.4 KiB
Python
74 lines
2.4 KiB
Python
"""Dataset storage serialization helpers (HDF5 / memory-mapped binary)."""
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import json
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import os
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from pathlib import Path
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from typing import Dict, List
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import h5py
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import numpy as np
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import torch
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from torch import Tensor
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def save_h5(file_path: str, file_name: str, tensor_group: Dict[str, List[Tensor]]):
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os.makedirs(file_path, exist_ok=True)
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full_file_path = os.path.join(file_path, f"{file_name}.h5")
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with h5py.File(full_file_path, "w") as f:
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for key, tensors in tensor_group.items():
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grp = f.create_group(key)
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for idx, tensor in enumerate(tensors):
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arr = tensor.cpu().numpy()
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grp.create_dataset(f"data_{idx}", data=arr)
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def load_h5(file_path: str, share_memory=True) -> Dict[str, List[Tensor]]:
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tensor_group: Dict[str, List[Tensor]] = {}
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root_path = Path(file_path)
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h5_files = list(root_path.rglob("*.h5")) + list(root_path.rglob("*.hdf5"))
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for h5_file in h5_files:
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with h5py.File(h5_file, "r") as f:
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for key in f.keys():
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grp = f[key]
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dsets = []
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for dset_name in grp.keys():
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dset = grp[dset_name]
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tensor = torch.from_numpy(dset[:])
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if share_memory:
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tensor = tensor.share_memory_()
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dsets.append(tensor)
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if tensor_group.get(key) is None:
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tensor_group[key] = []
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tensor_group[key].extend(dsets)
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return tensor_group
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def save_bin(file_path: str, tensor_group: Dict[str, List[Tensor]]):
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os.makedirs(file_path, exist_ok=True)
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meta = {}
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for key, tensors in tensor_group.items():
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cat = torch.cat(tensors, dim=0)
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meta[key] = {"shape": list(cat.shape), "dtype": str(cat.dtype).split(".")[-1]}
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np.asarray(cat.cpu().numpy()).tofile(os.path.join(file_path, f"{key}.bin"))
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with open(os.path.join(file_path, "meta.json"), "w") as f:
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json.dump(meta, f)
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def load_bin(file_path: str) -> Dict[str, List[Tensor]]:
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with open(os.path.join(file_path, "meta.json"), "r") as f:
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meta = json.load(f)
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segments: Dict[str, List[Tensor]] = {}
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for key, info in meta.items():
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arr = np.memmap(
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os.path.join(file_path, f"{key}.bin"),
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dtype=info["dtype"],
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mode="r+",
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shape=tuple(info["shape"]),
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)
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segments[key] = [torch.from_numpy(arr)]
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return segments
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