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codesign_ppf.py
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codesign_ppf.py
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import os
import argparse
import copy
import json
from tqdm.auto import tqdm
from torch.utils.data import DataLoader
import torch
from ppflow.datasets import get_dataset
from ppflow.models import get_model
from ppflow.modules.common.geometry import manifold_to_euclid
from ppflow.utils.protein.writers import save_pdb
from ppflow.utils.train import recursive_to
from ppflow.utils.misc import *
from ppflow.utils.data import *
from ppflow.utils.transforms import *
from ppflow.utils.inference import *
from ppflow.utils.transforms import _index_select_data
import os
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--index', type=int, default=0)
parser.add_argument('-c', '--config', type=str, default='./configs/test/codesign_ppflow.yml')
parser.add_argument('-o', '--out_root', type=str, default='./results/ppflow')
parser.add_argument('-t', '--tag', type=str, default='124')
parser.add_argument('-s', '--seed', type=int, default=None)
parser.add_argument('-d', '--device', type=str, default='cuda')
parser.add_argument('-b', '--batch_size', type=int, default=64)
parser.add_argument('-ckpt', '--checkpoint', type=str, default=None)
args = parser.parse_args()
# Load configs
config, config_name = load_config(args)
if args.checkpoint is not None:
config.model.checkpoint = args.checkpoint
tag_postfix = '_%s' % args.tag if args.tag else ''
seed_all(args.seed if args.seed is not None else config.sampling.seed)
# Testset
dataset = get_dataset(config.dataset, split='test')
dr = os.path.join(args.out_root, config_name + tag_postfix)
if not os.path.exists(dr):
os.makedirs(dr, exist_ok=True)
mark = 0
for i in range(mark, len(dataset)):
args.index = i
get_structure = lambda: dataset[args.index]
get_raw_structure = lambda: dataset.get_raw(args.index)
# Logging
structure_ = get_structure()
raw_strcuture_ = get_raw_structure()
structure_id = structure_['pdb_name']
log_dir = get_new_log_dir(dr, prefix='%04d_%s' % (args.index, structure_id))
logger = get_logger('sample', log_dir)
logger.info('Data ID: %s' % structure_['pdb_name'])
data_native = raw_strcuture_['peptide']
save_pdb(data_native, os.path.join(log_dir, 'reference.pdb'))
# Load checkpoint and model
logger.info('Loading model config and checkpoints: %s' % (config.model.checkpoint))
ckpt = torch.load(config.model.checkpoint, map_location='cpu')
cfg_ckpt = ckpt['config']
model = get_model(cfg_ckpt.model).to(args.device)
lsd = model.load_state_dict(ckpt['model'])
logger.info(str(lsd))
# Start sampling
collate_fn = PaddingCollate(eight=False)
data_list_repeat = [ structure_ ] * config.sampling.num_samples
loader = DataLoader(data_list_repeat,
batch_size=args.batch_size,
shuffle=False,
collate_fn=collate_fn)
count = 0
for batch in tqdm(loader, desc=structure_id, dynamic_ncols=True):
torch.set_grad_enabled(False)
model.eval()
batch = recursive_to(batch, args.device)
traj_batch = model.sample(batch, sample_opt={
'pbar': True,
'sample_structure': config.sampling.sample_structure,
'sample_sequence': config.sampling.sample_sequence,
'num_steps': config.sampling.num_steps
})
pos_atom_new_bb3, pos_atom_new_bb4 = manifold_to_euclid(
traj_batch[config.sampling.num_steps][0],
traj_batch[config.sampling.num_steps][1],
traj_batch[config.sampling.num_steps][2],
batch['pos_heavyatom'][:,:,:4],
batch['mask_gen_pos'],
bb4=True)
aa_new = traj_batch[config.sampling.num_steps][3] # 0: Last sampling step. 2: Amino acid.
aa_new = aa_new.cpu()
pos_atom_new_bb4 = pos_atom_new_bb4.cpu()[:,:,:4]
pos_atom_new_bb3 = pos_atom_new_bb3.cpu()
mask_atom_new = batch['mask_gen_pos'][:,:,None].repeat(1,1,4).cpu()
for i in range(aa_new.size(0)):
peptide_patch_idx = torch.where(batch['mask_gen_pos'][i])[0].cpu()
data_tmpl = _index_select_data(structure_, peptide_patch_idx)
aa = aa_new[i][peptide_patch_idx]
mask_ha = mask_atom_new[i][peptide_patch_idx]
pos_ha = pos_atom_new_bb4[i][peptide_patch_idx]
pos_ha_translate = pos_ha + structure_['pos_center_org']
save_path = os.path.join(log_dir, '%04d.pdb' % (count, ))
save_pdb({
'chain_nb': data_tmpl['chain_nb'],
'chain_id': data_tmpl['chain_id'],
'resseq': data_tmpl['resseq'],
'icode': data_tmpl['icode'],
# Generated
'aa': aa,
'mask_heavyatom': mask_ha,
'pos_heavyatom': pos_ha_translate,
}, path=save_path)
pos_ha = pos_atom_new_bb3[i][peptide_patch_idx]
pos_ha_translate = pos_ha + structure_['pos_center_org']
save_path = os.path.join(log_dir, '%04d_bb3.pdb' % (count, ))
save_pdb({
'chain_nb': data_tmpl['chain_nb'],
'chain_id': data_tmpl['chain_id'],
'resseq': data_tmpl['resseq'],
'icode': data_tmpl['icode'],
# Generated
'aa': aa,
'mask_heavyatom': mask_ha,
'pos_heavyatom': pos_ha_translate,
}, path=save_path)
# pos_ha = pos_atom_new_bb3[i][peptide_patch_idx]
# pos_ha_redock = pos_ha - pos_ha.reshape(-1,3).mean(dim=0, keepdim=True)+ structure_['pos_center_lig']
# save_path = os.path.join(log_dir, '%04d_bb3_redock.pdb' % (count, ))
# save_pdb({
# 'chain_nb': data_tmpl['chain_nb'],
# 'chain_id': data_tmpl['chain_id'],
# 'resseq': data_tmpl['resseq'],
# 'icode': data_tmpl['icode'],
# # Generated
# 'aa': aa,
# 'mask_heavyatom': mask_ha,
# 'pos_heavyatom': pos_ha_redock,
# }, path=save_path)
count += 1
logger.info('Finished.\n')
if __name__ == '__main__':
main()