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f15d29e | 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 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import logging
import os
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Sequence
from zipfile import ZipFile
import ase.io
import hydra
import numpy as np
import torch
from pymatgen.core import Lattice, Structure
from pymatgen.io.ase import AseAtomsAdaptor
from ...common.globals import (
GENERATED_CRYSTALS_EXTXYZ_FILE_NAME,
GENERATED_CRYSTALS_ZIP_FILE_NAME,
)
from ...common.utils.data_classes import MatterGenCheckpointInfo
from ...common.utils.globals import get_device
from ...diffusion.lightning_module import DiffusionLightningModule
# logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def make_structure(
lengths: torch.Tensor,
angles: torch.Tensor,
atom_types: torch.Tensor,
frac_coords: torch.Tensor,
) -> Structure:
return Structure(
lattice=Lattice.from_parameters(
**{a: v for a, v in zip(["a", "b", "c"], lengths)},
**{a: v for a, v in zip(["alpha", "beta", "gamma"], angles)},
),
species=atom_types,
coords=frac_coords,
coords_are_cartesian=False,
)
def load_model_diffusion(
args: MatterGenCheckpointInfo,
) -> DiffusionLightningModule:
assert args.load_epoch is not None
ckpt = args.checkpoint_path
logger.info(f"Loading model from checkpoint: {ckpt}")
cfg = args.config
try:
model, incompatible_keys = DiffusionLightningModule.load_from_checkpoint_and_config(
ckpt,
map_location=get_device(),
config=cfg.lightning_module,
strict=args.strict_checkpoint_loading,
)
except hydra.errors.HydraException as e:
raise
if len(incompatible_keys.unexpected_keys) > 0:
raise ValueError(f"Unexpected keys in checkpoint: {incompatible_keys.unexpected_keys}.")
if len(incompatible_keys.missing_keys) > 0:
raise ValueError(f"Missing keys in checkpoint: {incompatible_keys.missing_keys}.")
return model
def get_crystals_list(
frac_coords, atom_types, lengths, angles, num_atoms
) -> list[dict[str, np.ndarray]]:
"""
args:
frac_coords: (num_atoms, 3)
atom_types: (num_atoms)
lengths: (num_crystals)
angles: (num_crystals)
num_atoms: (num_crystals)
"""
assert frac_coords.size(0) == atom_types.size(0) == num_atoms.sum()
assert lengths.size(0) == angles.size(0) == num_atoms.size(0)
start_idx = 0
crystal_array_list = []
for batch_idx, num_atom in enumerate(num_atoms.tolist()):
cur_frac_coords = frac_coords.narrow(0, start_idx, num_atom)
cur_atom_types = atom_types.narrow(0, start_idx, num_atom)
cur_lengths = lengths[batch_idx]
cur_angles = angles[batch_idx]
crystal_array_list.append(
{
"frac_coords": cur_frac_coords.detach().cpu().numpy(),
"atom_types": cur_atom_types.detach().cpu().numpy(),
"lengths": cur_lengths.detach().cpu().numpy(),
"angles": cur_angles.detach().cpu().numpy(),
}
)
start_idx = start_idx + num_atom
return crystal_array_list
def save_structures(output_path: Path, structures: Sequence[Structure]) -> None:
"""Save structures to disk in a extxyz file and a compressed zip file containing cif files.
Args:
output_path: path to a directory where the results are written.
structures: sequence of structures.
"""
ase_atoms = [AseAtomsAdaptor.get_atoms(x) for x in structures]
try:
ase.io.write(output_path / GENERATED_CRYSTALS_EXTXYZ_FILE_NAME, ase_atoms)
with ZipFile(output_path / GENERATED_CRYSTALS_ZIP_FILE_NAME, "w") as zip_obj:
for ix, ase_atom in enumerate(ase_atoms):
ase.io.write(f"/tmp/gen_{ix}.cif", ase_atom, format="cif")
zip_obj.write(f"/tmp/gen_{ix}.cif", arcname=f"gen_{ix}.cif")
except IOError as e:
print(f"Got error {e} writing the generated structures to disk.")
def load_structures(input_path: Path) -> Sequence[Structure]:
"""Load structures from disk.
Args:
output_path: path to a file or directory where the results are written.
Returns:
sequence of structures.
"""
# if the path is an xyz or extxyz file, read it directly
if input_path.suffix == ".xyz" or input_path.suffix == ".extxyz":
ase_atoms = ase.io.read(input_path, ":")
return [AseAtomsAdaptor.get_structure(x) for x in ase_atoms]
# if the path is a zipped folder, extract it into a temporary directory
elif input_path.suffix == ".zip":
with TemporaryDirectory() as tmpdirname:
with ZipFile(input_path, "r") as zip_obj:
zip_obj.extractall(tmpdirname)
return extract_structures_from_folder(tmpdirname)
# if the path is a directory, read all files in it
elif input_path.is_dir():
return extract_structures_from_folder(input_path)
else:
raise ValueError(f"Invalid input path {input_path}")
def extract_structures_from_folder(dirname: str) -> Sequence[Structure]:
structures = []
for filename in os.listdir(dirname):
if filename.endswith(".cif"):
try:
structures.append(Structure.from_file(f"{dirname}/{filename}"))
except ValueError as e:
logger.warning(f"Failed to read {filename} as a CIF file: {e}")
elif filename.endswith(".extxyz") or filename.endswith(".xyz"):
ase_atoms = ase.io.read(
f"{dirname}/{filename}", 0
) # We assume that the file contains only one structure
structures.append(AseAtomsAdaptor.get_structure(ase_atoms))
return structures
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