File size: 4,793 Bytes
b78a213 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | """
Creates LMDB files with extracted graph features from provided *.extxyz files
for the S2EF task.
"""
import argparse
import glob
import multiprocessing as mp
import os
import pickle
import random
import sys
import ase.io
import lmdb
import numpy as np
import torch
from tqdm import tqdm
from ocpmodels.preprocessing import AtomsToGraphs
def write_images_to_lmdb(mp_arg):
a2g, db_path, samples, sampled_ids, idx, pid, args = mp_arg
db = lmdb.open(
db_path,
map_size=1099511627776 * 2,
subdir=False,
meminit=False,
map_async=True,
)
pbar = tqdm(
total=5000 * len(samples),
position=pid,
desc="Preprocessing data into LMDBs",
)
for sample in samples:
traj_logs = open(sample, "r").read().splitlines()
xyz_idx = os.path.splitext(os.path.basename(sample))[0]
traj_path = os.path.join(args.data_path, f"{xyz_idx}.extxyz")
traj_frames = ase.io.read(traj_path, ":")
for i, frame in enumerate(traj_frames):
frame_log = traj_logs[i].split(",")
sid = int(frame_log[0].split("random")[1])
fid = int(frame_log[1].split("frame")[1])
data_object = a2g.convert(frame)
# add atom tags
data_object.tags = torch.LongTensor(frame.get_tags())
data_object.sid = sid
data_object.fid = fid
# subtract off reference energy
if args.ref_energy and not args.test_data:
ref_energy = float(frame_log[2])
data_object.y -= ref_energy
txn = db.begin(write=True)
txn.put(
f"{idx}".encode("ascii"),
pickle.dumps(data_object, protocol=-1),
)
txn.commit()
idx += 1
sampled_ids.append(",".join(frame_log[:2]) + "\n")
pbar.update(1)
# Save count of objects in lmdb.
txn = db.begin(write=True)
txn.put("length".encode("ascii"), pickle.dumps(idx, protocol=-1))
txn.commit()
db.sync()
db.close()
return sampled_ids, idx
def main(args):
xyz_logs = glob.glob(os.path.join(args.data_path, "*.txt"))
if not xyz_logs:
raise RuntimeError("No *.txt files found. Did you uncompress?")
if args.num_workers > len(xyz_logs):
args.num_workers = len(xyz_logs)
# Initialize feature extractor.
a2g = AtomsToGraphs(
max_neigh=50,
radius=6,
r_energy=not args.test_data,
r_forces=not args.test_data,
r_fixed=True,
r_distances=False,
r_edges=args.get_edges,
)
# Create output directory if it doesn't exist.
os.makedirs(os.path.join(args.out_path), exist_ok=True)
# Initialize lmdb paths
db_paths = [
os.path.join(args.out_path, "data.%04d.lmdb" % i)
for i in range(args.num_workers)
]
# Chunk the trajectories into args.num_workers splits
chunked_txt_files = np.array_split(xyz_logs, args.num_workers)
# Extract features
sampled_ids, idx = [[]] * args.num_workers, [0] * args.num_workers
pool = mp.Pool(args.num_workers)
mp_args = [
(
a2g,
db_paths[i],
chunked_txt_files[i],
sampled_ids[i],
idx[i],
i,
args,
)
for i in range(args.num_workers)
]
op = list(zip(*pool.imap(write_images_to_lmdb, mp_args)))
sampled_ids, idx = list(op[0]), list(op[1])
# Log sampled image, trajectory trace
for j, i in enumerate(range(args.num_workers)):
ids_log = open(
os.path.join(args.out_path, "data_log.%04d.txt" % i), "w"
)
ids_log.writelines(sampled_ids[j])
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"--data-path",
help="Path to dir containing *.extxyz and *.txt files",
)
parser.add_argument(
"--out-path",
help="Directory to save extracted features. Will create if doesn't exist",
)
parser.add_argument(
"--get-edges",
action="store_true",
help="Store edge indices in LMDB, ~10x storage requirement. Default: compute edge indices on-the-fly.",
)
parser.add_argument(
"--num-workers",
type=int,
default=1,
help="No. of feature-extracting processes or no. of dataset chunks",
)
parser.add_argument(
"--ref-energy", action="store_true", help="Subtract reference energies"
)
parser.add_argument(
"--test-data",
action="store_true",
help="Is data being processed test data?",
)
return parser
if __name__ == "__main__":
parser = get_parser()
args = parser.parse_args()
main(args)
|