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import os.path as osp |
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import numpy as np |
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import torch |
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from pytorch_lightning.utilities import rank_zero_warn |
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from torch_geometric.data import Data, InMemoryDataset, download_url |
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from tqdm import tqdm |
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class MD17(InMemoryDataset): |
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""" |
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Machine learning of accurate energy-conserving molecular force fields (Chmiela et al. 2017) |
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This class provides functionality for loading MD trajectories from the original dataset, not the revised versions. |
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See http://www.quantum-machine.org/gdml/#datasets for details. |
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""" |
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raw_url = "http://www.quantum-machine.org/gdml/data/npz/" |
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molecule_files = dict( |
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aspirin="md17_aspirin.npz", |
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ethanol="md17_ethanol.npz", |
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malonaldehyde="md17_malonaldehyde.npz", |
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naphthalene="md17_naphthalene.npz", |
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salicylic_acid="md17_salicylic.npz", |
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toluene="md17_toluene.npz", |
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uracil="md17_uracil.npz", |
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) |
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available_molecules = list(molecule_files.keys()) |
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def __init__(self, root, transform=None, pre_transform=None, dataset_arg=None): |
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assert dataset_arg is not None, ( |
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"Please provide the desired comma separated molecule(s) through" |
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f"'dataset_arg'. Available molecules are {', '.join(MD17.available_molecules)} " |
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"or 'all' to train on the combined dataset." |
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) |
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if dataset_arg == "all": |
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dataset_arg = ",".join(MD17.available_molecules) |
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self.molecules = dataset_arg.split(",") |
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if len(self.molecules) > 1: |
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rank_zero_warn( |
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"MD17 molecules have different reference energies, " |
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"which is not accounted for during training." |
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) |
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super(MD17, self).__init__(osp.join(root, dataset_arg), transform, pre_transform) |
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self.offsets = [0] |
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self.data_all, self.slices_all = [], [] |
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for path in self.processed_paths: |
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data, slices = torch.load(path) |
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self.data_all.append(data) |
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self.slices_all.append(slices) |
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self.offsets.append(len(slices[list(slices.keys())[0]]) - 1 + self.offsets[-1]) |
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def len(self): |
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return sum(len(slices[list(slices.keys())[0]]) - 1 for slices in self.slices_all) |
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def get(self, idx): |
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data_idx = 0 |
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while data_idx < len(self.data_all) - 1 and idx >= self.offsets[data_idx + 1]: |
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data_idx += 1 |
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self.data = self.data_all[data_idx] |
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self.slices = self.slices_all[data_idx] |
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return super(MD17, self).get(idx - self.offsets[data_idx]) |
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@property |
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def raw_file_names(self): |
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return [MD17.molecule_files[mol] for mol in self.molecules] |
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@property |
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def processed_file_names(self): |
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return [f"md17-{mol}.pt" for mol in self.molecules] |
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def download(self): |
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for file_name in self.raw_file_names: |
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download_url(MD17.raw_url + file_name, self.raw_dir) |
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def process(self): |
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for path, processed_path in zip(self.raw_paths, self.processed_paths): |
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data_npz = np.load(path) |
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z = torch.from_numpy(data_npz["z"]).long() |
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positions = torch.from_numpy(data_npz["R"]).float() |
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energies = torch.from_numpy(data_npz["E"]).float() |
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forces = torch.from_numpy(data_npz["F"]).float() |
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samples = [] |
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for pos, y, dy in tqdm(zip(positions, energies, forces), total=energies.size(0)): |
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data = Data(z=z, pos=pos, y=y.unsqueeze(1), dy=dy) |
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if self.pre_filter is not None: |
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data = self.pre_filter(data) |
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if self.pre_transform is not None: |
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data = self.pre_transform(data) |
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samples.append(data) |
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data, slices = self.collate(samples) |
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torch.save((data, slices), processed_path) |
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