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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,11 @@ | ||
| from torch_geometric.datasets import InstructMolDataset | ||
| from torch_geometric.testing import onlyFullTest, withPackage | ||
|
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||
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| @onlyFullTest | ||
| @withPackage('rdkit') | ||
| def test_instruct_mol_dataset(): | ||
| dataset = InstructMolDataset(root='./data/InstructMol') | ||
| assert len(dataset) == 326689 | ||
| assert dataset.num_edge_features == 4 | ||
| assert dataset.num_node_features == 6 |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,134 @@ | ||
| import json | ||
| import sys | ||
| from typing import Callable, List, Optional | ||
|
|
||
| import torch | ||
| from tqdm import tqdm | ||
|
|
||
| from torch_geometric.data import Data, InMemoryDataset | ||
| from torch_geometric.io import fs | ||
| from torch_geometric.utils import one_hot | ||
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| class InstructMolDataset(InMemoryDataset): | ||
| r"""The dataset from the `"InstructMol: Multi-Modal Integration for | ||
| Building a Versatile and Reliable Molecular Assistant in Drug Discovery" | ||
| <https://arxiv.org/pdf/2311.16208>`_ paper. | ||
| Args: | ||
| root (str): Root directory where the dataset should be saved. | ||
| transform (callable, optional): A function/transform that takes in an | ||
| :obj:`torch_geometric.data.Data` object and returns a transformed | ||
| version. The data object will be transformed before every access. | ||
| (default: :obj:`None`) | ||
| pre_transform (callable, optional): A function/transform that takes in | ||
| an :obj:`torch_geometric.data.Data` object and returns a | ||
| transformed version. The data object will be transformed before | ||
| being saved to disk. (default: :obj:`None`) | ||
| pre_filter (callable, optional): A function that takes in an | ||
| :obj:`torch_geometric.data.Data` object and returns a boolean | ||
| value, indicating whether the data object should be included in the | ||
| final dataset. (default: :obj:`None`) | ||
| force_reload (bool, optional): Whether to re-process the dataset. | ||
| (default: :obj:`False`) | ||
| """ | ||
| raw_url = 'https://huggingface.co/datasets/OpenMol/PubChemSFT/blob/main' | ||
|
|
||
| def __init__( | ||
| self, | ||
| root: str, | ||
| transform: Optional[Callable] = None, | ||
| pre_transform: Optional[Callable] = None, | ||
| pre_filter: Optional[Callable] = None, | ||
| force_reload: bool = False, | ||
| ): | ||
| super().__init__(root, transform, pre_transform, pre_filter, | ||
| force_reload=force_reload) | ||
| self.load(self.processed_paths[0]) | ||
|
|
||
| @property | ||
| def raw_file_names(self) -> List[str]: | ||
| return ['all_clean.json'] | ||
|
|
||
| @property | ||
| def processed_file_names(self) -> List[str]: | ||
| return ['data.pt'] | ||
|
|
||
| def download(self) -> None: | ||
| print('downloading dataset...') | ||
| fs.cp(f'{self.raw_url}/all_clean.json', self.raw_dir) | ||
|
|
||
| def process(self) -> None: | ||
| try: | ||
| from rdkit import Chem | ||
| from rdkit.Chem.rdchem import BondType as BT | ||
| WITH_RDKIT = True | ||
|
|
||
| except ImportError: | ||
| WITH_RDKIT = False | ||
|
|
||
| if not WITH_RDKIT: | ||
| print(("Using a pre-processed version of the dataset. Please " | ||
| "install 'rdkit' to alternatively process the raw data."), | ||
| file=sys.stderr) | ||
|
|
||
| data_list = fs.torch_load(self.raw_paths[0]) | ||
| data_list = [Data(**data_dict) for data_dict in data_list] | ||
|
|
||
| if self.pre_filter is not None: | ||
| data_list = [d for d in data_list if self.pre_filter(d)] | ||
|
|
||
| if self.pre_transform is not None: | ||
| data_list = [self.pre_transform(d) for d in data_list] | ||
|
|
||
| self.save(data_list, self.processed_paths[0]) | ||
| return | ||
|
|
||
| # types of atom and bond | ||
| types = {'H': 0, 'C': 1, 'N': 2, 'O': 3, 'F': 4, 'Unknow': 5} | ||
| bonds = {BT.SINGLE: 0, BT.DOUBLE: 1, BT.TRIPLE: 2, BT.AROMATIC: 3} | ||
|
|
||
| # load data | ||
| mols = json.load(open(f'{self.raw_dir}/all_clean.json')) | ||
|
|
||
| data_list = [] | ||
| for smiles, qa_pairs in tqdm(mols.items(), total=len(mols)): | ||
| mol = Chem.MolFromSmiles(smiles) | ||
| if mol is None: | ||
| continue | ||
|
|
||
| x: torch.Tensor = torch.tensor([ | ||
| types[atom.GetSymbol()] if atom.GetSymbol() in types else 5 | ||
| for atom in mol.GetAtoms() | ||
| ]) | ||
| x = one_hot(x, num_classes=len(types), dtype=torch.float) | ||
|
|
||
| rows, cols, edge_types = [], [], [] | ||
| for bond in mol.GetBonds(): | ||
| i, j = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx() | ||
| edge_types += [bonds[bond.GetBondType()]] * 2 | ||
| rows += [i, j] | ||
| cols += [j, i] | ||
|
|
||
| edge_index = torch.tensor([rows, cols], dtype=torch.long) | ||
| edge_type = torch.tensor(edge_types, dtype=torch.long) | ||
| edge_attr = one_hot(edge_type, num_classes=len(bonds)) | ||
|
|
||
| for question, answer in qa_pairs: | ||
| data = Data( | ||
| x=x, | ||
| edge_index=edge_index, | ||
| edge_attr=edge_attr, | ||
| smiles=smiles, | ||
| instruction=question, | ||
| y=answer, | ||
| ) | ||
|
|
||
| if self.pre_filter is not None and not self.pre_filter(data): | ||
| continue | ||
| if self.pre_transform is not None: | ||
| data = self.pre_transform(data) | ||
|
|
||
| data_list.append(data) | ||
|
|
||
| self.save(data_list, self.processed_paths[0]) |
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