| import os.path as osp |
|
|
| import numpy as np |
| import torch |
| from torch_geometric.data import Data, InMemoryDataset, download_url |
| from tqdm import tqdm |
|
|
|
|
| class MD22(InMemoryDataset): |
| def __init__(self, root, dataset_arg=None, transform=None, pre_transform=None): |
| |
| self.dataset_arg = dataset_arg |
| |
| super(MD22, self).__init__(osp.join(root, dataset_arg), transform, pre_transform) |
| |
| self.data, self.slices = torch.load(self.processed_paths[0]) |
| |
| @property |
| def molecule_names(self): |
| |
| molecule_names = dict( |
| Ac_Ala3_NHMe="md22_Ac-Ala3-NHMe.npz", |
| DHA="md22_DHA.npz", |
| stachyose="md22_stachyose.npz", |
| AT_AT="md22_AT-AT.npz", |
| AT_AT_CG_CG="md22_AT-AT-CG-CG.npz", |
| buckyball_catcher="md22_buckyball-catcher.npz", |
| double_walled_nanotube="md22_dw_nanotube.npz" |
| ) |
|
|
| return molecule_names |
|
|
| @property |
| def raw_file_names(self): |
| return [self.molecule_names[self.dataset_arg]] |
|
|
| @property |
| def processed_file_names(self): |
| return [f"md22_{self.dataset_arg}.pt"] |
| |
| @property |
| def base_url(self): |
| return "http://www.quantum-machine.org/gdml/data/npz/" |
|
|
| def download(self): |
| |
| download_url(self.base_url + self.molecule_names[self.dataset_arg], self.raw_dir) |
| |
| def process(self): |
| for path, processed_path in zip(self.raw_paths, self.processed_paths): |
| data_npz = np.load(path) |
| z = torch.from_numpy(data_npz["z"]).long() |
| positions = torch.from_numpy(data_npz["R"]).float() |
| energies = torch.from_numpy(data_npz["E"]).float() |
| forces = torch.from_numpy(data_npz["F"]).float() |
|
|
| samples = [] |
| for pos, y, dy in tqdm(zip(positions, energies, forces), total=energies.size(0)): |
| |
| data = Data(z=z, pos=pos, y=y.unsqueeze(1), dy=dy) |
|
|
| if self.pre_filter is not None: |
| data = self.pre_filter(data) |
|
|
| if self.pre_transform is not None: |
| data = self.pre_transform(data) |
| |
| samples.append(data) |
|
|
| data, slices = self.collate(samples) |
| torch.save((data, slices), processed_path) |
| |
| @property |
| def molecule_splits(self): |
| """ |
| Splits refer to MD22 https://arxiv.org/pdf/2209.14865.pdf |
| """ |
| return dict( |
| Ac_Ala3_NHMe=6000, |
| DHA=8000, |
| stachyose=8000, |
| AT_AT=3000, |
| AT_AT_CG_CG=2000, |
| buckyball_catcher=600, |
| double_walled_nanotube=800 |
| ) |
