File size: 3,055 Bytes
4947683 | 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 | import time
import argparse
from diffcsp.script_utils import GenDataset
import torch
from tqdm import tqdm
from torch.optim import Adam
from pathlib import Path
from types import SimpleNamespace
from torch_geometric.data import Batch, DataLoader
from diffcsp.eval_utils import load_model, lattices_to_params_shape, get_crystals_list, recommand_step_lr
from pymatgen.core.structure import Structure
from pymatgen.core.lattice import Lattice
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.io.cif import CifWriter
from pyxtal.symmetry import Group
import chemparse
from p_tqdm import p_map
import pdb
import os
def diffusion(loader, model, step_lr):
frac_coords = []
num_atoms = []
atom_types = []
lattices = []
input_data_list = []
for idx, batch in enumerate(loader):
if torch.cuda.is_available():
batch.cuda()
outputs, traj = model.sample(batch, step_lr = step_lr)
frac_coords.append(outputs['frac_coords'].detach().cpu())
num_atoms.append(outputs['num_atoms'].detach().cpu())
atom_types.append(outputs['atom_types'].detach().cpu())
lattices.append(outputs['lattices'].detach().cpu())
frac_coords = torch.cat(frac_coords, dim=0)
num_atoms = torch.cat(num_atoms, dim=0)
atom_types = torch.cat(atom_types, dim=0)
lattices = torch.cat(lattices, dim=0)
lengths, angles = lattices_to_params_shape(lattices)
return (
frac_coords, atom_types, lattices, lengths, angles, num_atoms
)
def main(args):
# load_data if do reconstruction.
model_path = Path(args.model_path)
model, _, cfg = load_model(
model_path,
load_data=False,
)
if torch.cuda.is_available():
model.to('cuda')
print('Evaluate the diffusion model.')
test_set = GenDataset(args.dataset, args.batch_size * args.num_batches_to_samples)
test_loader = DataLoader(test_set, batch_size = args.batch_size)
step_lr = args.step_lr if args.step_lr >= 0 else recommand_step_lr['gen'][args.dataset]
print(step_lr)
start_time = time.time()
(frac_coords, atom_types, lattices, lengths, angles, num_atoms) = diffusion(test_loader, model, step_lr)
if args.label == '':
gen_out_name = 'eval_gen.pt'
else:
gen_out_name = f'eval_gen_{args.label}.pt'
torch.save({
'eval_setting': args,
'frac_coords': frac_coords,
'num_atoms': num_atoms,
'atom_types': atom_types,
'lengths': lengths,
'angles': angles,
}, model_path / gen_out_name)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', required=True)
parser.add_argument('--dataset', required=True)
parser.add_argument('--step_lr', default=-1, type=float)
parser.add_argument('--num_batches_to_samples', default=20, type=int)
parser.add_argument('--batch_size', default=500, type=int)
parser.add_argument('--label', default='')
args = parser.parse_args()
main(args)
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