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data/ff_lmdb.py

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-"""
-Copyright (c) Facebook, Inc. and its affiliates.
-
-This source code is licensed under the MIT license found in the
-LICENSE file in the root directory of this source tree.
-"""
-
-import bisect
-import pickle
-from pathlib import Path
-
-import lmdb
-import numpy as np
-from torch.utils.data import Dataset
-
-# from torch_geometric.data import Batch
-
-
-class LmdbDataset(Dataset):
-    r"""Dataset class to load from LMDB files containing relaxation
-    trajectories or single point computations.
-
-    Useful for Structure to Energy & Force (S2EF), Initial State to
-    Relaxed State (IS2RS), and Initial State to Relaxed Energy (IS2RE) tasks.
-
-    Args:
-            config (dict): Dataset configuration
-            transform (callable, optional): Data transform function.
-                    (default: :obj:`None`)
-    """
-
-    def __init__(self, src, transform=None, **kwargs):
-        super(LmdbDataset, self).__init__()
-
-        self.path = Path(src)
-        if not self.path.is_file():
-            db_paths = sorted(self.path.glob("*.lmdb"))
-            assert len(db_paths) > 0, f"No LMDBs found in '{self.path}'"
-
-            self.metadata_path = self.path / "metadata.npz"
-
-            self._keys, self.envs = [], []
-            for db_path in db_paths:
-                self.envs.append(self.connect_db(db_path))
-                length = pickle.loads(
-                    self.envs[-1].begin().get("length".encode("ascii"))
-                )
-                self._keys.append(list(range(length)))
-
-            keylens = [len(k) for k in self._keys]
-            self._keylen_cumulative = np.cumsum(keylens).tolist()
-            self.num_samples = sum(keylens)
-        else:
-            self.metadata_path = self.path.parent / "metadata.npz"
-            self.env = self.connect_db(self.path)
-            try:
-                # Try to get the stored length value first
-                self.num_samples = pickle.loads(
-                    self.env.begin().get("length".encode("ascii"))
-                )
-            except (TypeError, KeyError):
-                # Fallback to entries count if length key doesn't exist
-                self.num_samples = self.env.stat()["entries"]
-
-            self._keys = [f"{j}".encode("ascii") for j in range(self.num_samples)]
-
-        self.transform = transform
-
-    def __len__(self):
-        return self.num_samples
-
-    def __getitem__(self, idx):
-        if idx >= self.num_samples:
-            raise IndexError(
-                f"Index {idx} out of range for dataset with {self.num_samples} samples"
-            )
-
-        if not self.path.is_file():
-            # Figure out which db this should be indexed from.
-            db_idx = bisect.bisect(self._keylen_cumulative, idx)
-            # Extract index of element within that db.
-            el_idx = idx
-            if db_idx != 0:
-                el_idx = idx - self._keylen_cumulative[db_idx - 1]
-            assert el_idx >= 0
-
-            # Return features.
-            datapoint_pickled = (
-                self.envs[db_idx]
-                .begin()
-                .get(f"{self._keys[db_idx][el_idx]}".encode("ascii"))
-            )
-            data_object = pickle.loads(datapoint_pickled)
-            data_object.id = f"{db_idx}_{el_idx}"
-        else:
-            datapoint_pickled = self.env.begin().get(self._keys[idx])
-            if datapoint_pickled is None:
-                raise KeyError(f"No data found for index {idx}")
-            data_object = pickle.loads(datapoint_pickled)
-
-        if self.transform is not None:
-            data_object = self.transform(data_object)
-
-        return data_object
-
-    def connect_db(self, lmdb_path=None):
-        env = lmdb.open(
-            str(lmdb_path),
-            subdir=False,
-            readonly=True,
-            lock=False,
-            readahead=False,
-            meminit=False,
-            max_readers=1,
-            map_size=1099511627776 * 2,
-        )
-        return env
-
-    def close_db(self):
-        if not self.path.is_file():
-            for env in self.envs:
-                env.close()
-        else:
-            self.env.close()
-
-
-def remove_hessian_transform(data):
-    # Remove 'hessian' if present as attribute
-    if hasattr(data, "hessian"):
-        delattr(data, "hessian")
-    # Remove 'hessian' if present as key (for dict-like)
-    # if isinstance(data, dict) and 'hessian' in data:
-    #     del data['hessian']
-    return data
-
-
-def fix_hessian_eigen_transform(data):
-    """Fixes errors in old versions of the code.
-    Only needed for legacy compatibility.
-    You can probably remove this function.
-
-    Sizes of tensors must match except in dimension 0.
-
-    Hessian eigen information is stored as:
-    hessian_eigenvalues: torch.Size([2])
-    hessian_eigenvectors: torch.Size([2, N*3])
-
-    Instead save as:
-    hessian_eigenvalue_1: torch.Size([1])
-    hessian_eigenvalue_2: torch.Size([1])
-    hessian_eigenvector_1: torch.Size([N, 3])
-    hessian_eigenvector_2: torch.Size([N, 3])
-    """
-    # Check if hessian eigenvalue/eigenvector data exists
-    if hasattr(data, "hessian_eigenvalues") and hasattr(data, "hessian_eigenvectors"):
-        eigenvalues = data.hessian_eigenvalues
-        eigenvectors = data.hessian_eigenvectors
-
-        # Split eigenvalues into separate attributes
-        data.hessian_eigenvalue_1 = eigenvalues[0:1]  # Keep as [1] tensor
-        data.hessian_eigenvalue_2 = eigenvalues[1:2]  # Keep as [1] tensor
-
-        # Reshape and split eigenvectors from [2, N*3] to [N, 3] format
-        n_atoms = len(data.pos)  # Get number of atoms from positions
-        eigenvector_1 = eigenvectors[0].reshape(n_atoms, 3)
-        eigenvector_2 = eigenvectors[1].reshape(n_atoms, 3)
-
-        data.hessian_eigenvector_1 = eigenvector_1
-        data.hessian_eigenvector_2 = eigenvector_2
-
-        # Remove original attributes
-        delattr(data, "hessian_eigenvalues")
-        delattr(data, "hessian_eigenvectors")
-
-    # Remove batch artifacts manually
-    for key in ["batch", "ptr"]:
-        if hasattr(data, key):
-            delattr(data, key)
-
-    return data
-
-
-if __name__ == "__main__":
-    import os
-
-    dataset_dir = os.path.expanduser(
-        "~/.cache/kagglehub/datasets/yunhonghan/hessian-dataset-for-optimizing-reactive-mliphorm/versions/5/"
-    )
-    dataset_files = [
-        "ts1x-val.lmdb",
-        "ts1x_hess_train_big.lmdb",
-        "RGD1.lmdb",
-    ]
-    lmdb_path = os.path.join(dataset_dir, dataset_files[0])
-    lmdb_dataset = LmdbDataset(lmdb_path)
-    print("length of lmdb_dataset:", len(lmdb_dataset))
-    print("first element of lmdb_dataset:", lmdb_dataset[0])
-    print("first element of lmdb_dataset.pos:", lmdb_dataset[0].pos)
-    print("first element of lmdb_dataset.ae:", lmdb_dataset[0].ae)
-    first_elem = lmdb_dataset[0]
-    print("")
-    print("hasattr(first_elem, 'hessian'):", hasattr(first_elem, "hessian"))
-    print("'hessian' in first_elem:", "hessian" in first_elem)
-
-    # Test with transform that removes hessian
-    lmdb_dataset_no_hessian = LmdbDataset(lmdb_path, transform=remove_hessian_transform)
-    first_elem = lmdb_dataset_no_hessian[0]
-    print("")
-    print("hasattr(first_elem, 'hessian'):", hasattr(first_elem, "hessian"))
-    print("'hessian' in first_elem:", "hessian" in first_elem)
-
-    for fname in dataset_files:
-        path = os.path.join(dataset_dir, fname)
-        ds = LmdbDataset(path)
-        print(f"Size of {fname}: {len(ds)}")

data/sample_100-dft-hess-eigen.lmdb

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-size 2203648

data/sample_100.lmdb

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