import copy import numpy as np from rdkit.Chem import RemoveAllHs from torch_geometric.loader import DataLoader from tqdm import tqdm import torch from confidence.dataset import ListDataset from utils import so3, torus from utils.molecules_utils import get_symmetry_rmsd from utils.sampling import randomize_position, sampling from utils.diffusion_utils import get_t_schedule def loss_function(tr_pred, rot_pred, tor_pred, sidechain_pred, data, t_to_sigma, device, tr_weight=1, rot_weight=1, tor_weight=1, backbone_weight=0, sidechain_weight=0, apply_mean=True, no_torsion=False): tr_sigma, rot_sigma, tor_sigma = t_to_sigma( *[torch.cat([d.complex_t[noise_type] for d in data]) if device.type == 'cuda' else data.complex_t[noise_type] for noise_type in ['tr', 'rot', 'tor']]) mean_dims = (0, 1) if apply_mean else 1 # translation component tr_score = torch.cat([d.tr_score for d in data], dim=0) if device.type == 'cuda' else data.tr_score tr_sigma = tr_sigma.unsqueeze(-1) tr_loss = ((tr_pred.cpu() - tr_score.cpu()) ** 2 * tr_sigma.cpu() ** 2).mean(dim=mean_dims) tr_base_loss = (tr_score ** 2 * tr_sigma ** 2).mean(dim=mean_dims).detach() # rotation component rot_score = torch.cat([d.rot_score for d in data], dim=0) if device.type == 'cuda' else data.rot_score rot_score_norm = so3.score_norm(rot_sigma.cpu()).unsqueeze(-1) rot_loss = (((rot_pred.cpu() - rot_score.cpu()) / rot_score_norm) ** 2).mean(dim=mean_dims) rot_base_loss = ((rot_score.cpu() / rot_score_norm) ** 2).mean(dim=mean_dims).detach() # torsion component if not no_torsion: edge_tor_sigma = torch.from_numpy( np.concatenate([d.tor_sigma_edge for d in data] if device.type == 'cuda' else data.tor_sigma_edge)) tor_score = torch.cat([d.tor_score for d in data], dim=0) if device.type == 'cuda' else data.tor_score tor_score_norm2 = torch.tensor(torus.score_norm(edge_tor_sigma.cpu().numpy())).float() tor_loss = ((tor_pred.cpu() - tor_score.cpu()) ** 2 / tor_score_norm2) tor_base_loss = ((tor_score.cpu() ** 2 / tor_score_norm2)).detach() if apply_mean: tor_loss, tor_base_loss = tor_loss.mean() * torch.ones(1, dtype=torch.float), tor_base_loss.mean() * torch.ones(1, dtype=torch.float) else: index = torch.cat([torch.ones(d['ligand'].edge_mask.sum()) * i for i, d in enumerate(data)]).long() if device.type == 'cuda' else data['ligand'].batch[ data['ligand', 'ligand'].edge_index[0][data['ligand'].edge_mask]] num_graphs = len(data) if device.type == 'cuda' else data.num_graphs t_l, t_b_l, c = torch.zeros(num_graphs), torch.zeros(num_graphs), torch.zeros(num_graphs) c.index_add_(0, index, torch.ones(tor_loss.shape)) c = c + 0.0001 t_l.index_add_(0, index, tor_loss) t_b_l.index_add_(0, index, tor_base_loss) tor_loss, tor_base_loss = t_l / c, t_b_l / c else: if apply_mean: tor_loss, tor_base_loss = torch.zeros(1, dtype=torch.float), torch.zeros(1, dtype=torch.float) else: tor_loss, tor_base_loss = torch.zeros(len(rot_loss), dtype=torch.float), torch.zeros(len(rot_loss), dtype=torch.float) if backbone_weight > 0: backbone_vecs = torch.cat([d['receptor'].side_chain_vecs.cpu() for d in data], dim=0) if device.type == 'cuda' else data['receptor'].side_chain_vecs backbone_vecs = backbone_vecs[:, 4:] backbone_pred = sidechain_pred[:, 4:] backbone_base_loss = (backbone_vecs ** 2).detach().mean(dim=1) + 0.0001 backbone_loss = ((backbone_pred.cpu() - backbone_vecs) ** 2).mean(dim=1) / backbone_base_loss.mean() backbone_base_loss = backbone_base_loss / backbone_base_loss.mean() if apply_mean: backbone_loss, backbone_base_loss = backbone_loss.mean() * torch.ones(1, dtype=torch.float), backbone_base_loss.mean() * torch.ones(1, dtype=torch.float) else: index = torch.cat([torch.ones((d['receptor'].pos.shape[0])) * i for i, d in enumerate(data)], dim=0).long() if device.type == 'cuda' else data['receptor'].batch num_graphs = len(data) if device.type == 'cuda' else data.num_graphs s_l, s_b_l, c = torch.zeros(num_graphs), torch.zeros(num_graphs), torch.zeros(num_graphs) c.index_add_(0, index, torch.ones(backbone_loss.shape[0])) c = c + 0.0001 s_l.index_add_(0, index, backbone_loss) s_b_l.index_add_(0, index, backbone_base_loss) backbone_loss, backbone_base_loss = s_l / c, s_b_l / c else: if apply_mean: backbone_loss, backbone_base_loss = torch.zeros(1, dtype=torch.float), torch.zeros(1, dtype=torch.float) else: backbone_loss, backbone_base_loss = torch.zeros(len(rot_loss), dtype=torch.float), torch.zeros(len(rot_loss), dtype=torch.float) if sidechain_weight > 0: sidechain_vecs = torch.cat([d['receptor'].side_chain_vecs.cpu() for d in data], dim=0) if device.type == 'cuda' else data['receptor'].side_chain_vecs chi_angles = sidechain_vecs[:, :4].to(device) chi_pred = sidechain_pred[:, :4].to(device) chi_pred = torch.where(torch.isnan(chi_angles), torch.zeros_like(chi_angles, device=device), chi_pred) chi_angles = torch.where(torch.isnan(chi_angles), torch.zeros_like(chi_angles, device=device), chi_angles) difference = torch.abs(chi_pred - chi_angles) difference = torch.min(difference, 1 - difference) # angles are circular and 360 degrees = 1 sidechain_base_loss = (chi_angles ** 2).detach().mean(dim=1) + 0.0001 sidechain_loss = (difference ** 2).mean(dim=1) / sidechain_base_loss.mean() sidechain_base_loss = sidechain_base_loss / sidechain_base_loss.mean() if apply_mean: sidechain_loss, sidechain_base_loss = \ sidechain_loss.mean().cpu() * torch.ones(1, dtype=torch.float), \ sidechain_base_loss.mean().cpu() * torch.ones(1, dtype=torch.float) else: index = torch.cat([torch.ones((d['receptor'].pos.shape[0])) * i for i, d in enumerate(data)], dim=0).long() if device.type == 'cuda' else data['receptor'].batch num_graphs = len(data) if device.type == 'cuda' else data.num_graphs s_l, s_b_l, c = torch.zeros(num_graphs), torch.zeros(num_graphs), torch.zeros(num_graphs) c.index_add_(0, index, torch.ones(sidechain_loss.shape[0])) c = c + 0.0001 s_l.index_add_(0, index, sidechain_loss.cpu()) s_b_l.index_add_(0, index, sidechain_base_loss.cpu()) sidechain_loss, sidechain_base_loss = s_l / c, s_b_l / c else: if apply_mean: sidechain_loss, sidechain_base_loss = torch.zeros(1, dtype=torch.float), torch.zeros(1, dtype=torch.float) else: sidechain_loss, sidechain_base_loss = torch.zeros(len(rot_loss), dtype=torch.float), torch.zeros( len(rot_loss), dtype=torch.float) loss = tr_loss * tr_weight + rot_loss * rot_weight + tor_loss * tor_weight + sidechain_loss * sidechain_weight + backbone_loss * backbone_weight return loss, tr_loss.detach(), rot_loss.detach(), tor_loss.detach(), backbone_loss.detach(), sidechain_loss.detach(), \ tr_base_loss, rot_base_loss, tor_base_loss, backbone_base_loss, sidechain_base_loss class AverageMeter(): def __init__(self, types, unpooled_metrics=False, intervals=1): self.types = types self.intervals = intervals self.count = 0 if intervals == 1 else torch.zeros(len(types), intervals) self.acc = {t: torch.zeros(intervals) for t in types} self.unpooled_metrics = unpooled_metrics def add(self, vals, interval_idx=None): if self.intervals == 1: self.count += 1 if vals[0].dim() == 0 else len(vals[0]) for type_idx, v in enumerate(vals): self.acc[self.types[type_idx]] += v.sum().cpu() if self.unpooled_metrics else v.cpu() else: for type_idx, v in enumerate(vals): self.count[type_idx].index_add_(0, interval_idx[type_idx], torch.ones(len(v))) if not torch.allclose(v, torch.tensor(0.0)): self.acc[self.types[type_idx]].index_add_(0, interval_idx[type_idx], v) def summary(self): if self.intervals == 1: out = {k: v.item() / self.count for k, v in self.acc.items()} return out else: out = {} for i in range(self.intervals): for type_idx, k in enumerate(self.types): out['int' + str(i) + '_' + k] = ( list(self.acc.values())[type_idx][i] / self.count[type_idx][i]).item() return out def train_epoch(model, loader, optimizer, device, t_to_sigma, loss_fn, ema_weights): model.train() meter = AverageMeter(['loss', 'tr_loss', 'rot_loss', 'tor_loss', 'backbone_loss', 'sidechain_loss', 'tr_base_loss', 'rot_base_loss', 'tor_base_loss', 'backbone_base_loss', 'sidechain_base_loss']) for data in tqdm(loader, total=len(loader)): if device.type == 'cuda' and len(data) == 1 or device.type == 'cpu' and data.num_graphs == 1: print("Skipping batch of size 1 since otherwise batchnorm would not work.") continue optimizer.zero_grad() data = [d.to(device) for d in data] if device.type == 'cuda' else data try: tr_pred, rot_pred, tor_pred, sidechain_pred = model(data) loss_tuple = loss_fn(tr_pred, rot_pred, tor_pred, sidechain_pred, data=data, t_to_sigma=t_to_sigma, device=device) if loss_tuple is None: print("None loss tuple, skipping") continue loss = loss_tuple[0] if torch.any(torch.isnan(loss)): names = data.name if device.type == 'cpu' else [d.name for d in data] print("Nan loss, skipping batch with complexes", names) continue loss.backward() optimizer.step() if ema_weights is not None: ema_weights.update(model.parameters()) meter.add([loss.cpu().detach(), *loss_tuple[1:]]) except RuntimeError as e: if 'out of memory' in str(e): print('| WARNING: ran out of memory, skipping batch') for p in model.parameters(): if p.grad is not None: del p.grad # free some memory torch.cuda.empty_cache() continue elif 'Input mismatch' in str(e): print('| WARNING: weird torch_cluster error, skipping batch') for p in model.parameters(): if p.grad is not None: del p.grad # free some memory torch.cuda.empty_cache() continue else: #raise e print(e) continue return meter.summary() def test_epoch(model, loader, device, t_to_sigma, loss_fn, test_sigma_intervals=False): model.eval() meter = AverageMeter(['loss', 'tr_loss', 'rot_loss', 'tor_loss', 'backbone_loss', 'sidechain_loss', 'tr_base_loss', 'rot_base_loss', 'tor_base_loss', 'backbone_base_loss', 'sidechain_base_loss'], unpooled_metrics=True) if test_sigma_intervals: meter_all = AverageMeter( ['loss', 'tr_loss', 'rot_loss', 'tor_loss', 'backbone_loss', 'sidechain_loss', 'tr_base_loss', 'rot_base_loss', 'tor_base_loss', 'backbone_base_loss', 'sidechain_base_loss'], unpooled_metrics=True, intervals=10) for data in tqdm(loader, total=len(loader)): try: with torch.no_grad(): tr_pred, rot_pred, tor_pred, sidechain_pred = model(data) loss_tuple = loss_fn(tr_pred, rot_pred, tor_pred, sidechain_pred, data=data, t_to_sigma=t_to_sigma, apply_mean=False, device=device) if loss_tuple is None: continue meter.add([loss_tuple[0].cpu().detach(), *loss_tuple[1:]]) if test_sigma_intervals > 0: complex_t_tr, complex_t_rot, complex_t_tor = [torch.cat([data[i].complex_t[noise_type] for i in range(len(data))]) for noise_type in ['tr', 'rot', 'tor']] sigma_index_tr = torch.round(complex_t_tr.cpu() * (10 - 1)).long() sigma_index_rot = torch.round(complex_t_rot.cpu() * (10 - 1)).long() sigma_index_tor = torch.round(complex_t_tor.cpu() * (10 - 1)).long() meter_all.add([loss_tuple[0].cpu().detach(), *loss_tuple[1:]], [sigma_index_tr, sigma_index_tr, sigma_index_rot, sigma_index_tor, sigma_index_tr, sigma_index_tr, sigma_index_tr, sigma_index_rot, sigma_index_tor, sigma_index_tr, sigma_index_tr]) except RuntimeError as e: if 'out of memory' in str(e): print('| WARNING: ran out of memory, skipping batch') for p in model.parameters(): if p.grad is not None: del p.grad # free some memory torch.cuda.empty_cache() continue elif 'Input mismatch' in str(e): print('| WARNING: weird torch_cluster error, skipping batch') for p in model.parameters(): if p.grad is not None: del p.grad # free some memory torch.cuda.empty_cache() continue else: raise e print(e) continue out = meter.summary() if test_sigma_intervals > 0: out.update(meter_all.summary()) return out def inference_epoch_fix(model, complex_graphs, device, t_to_sigma, args): t_schedule = get_t_schedule(sigma_schedule='expbeta', inference_steps=args.inference_steps, inf_sched_alpha=1, inf_sched_beta=1) tr_schedule, rot_schedule, tor_schedule = t_schedule, t_schedule, t_schedule dataset = ListDataset(complex_graphs) loader = DataLoader(dataset=dataset, batch_size=1, shuffle=False) rmsds, min_rmsds = [], [] for orig_complex_graph in tqdm(loader): data_list = [copy.deepcopy(orig_complex_graph) for _ in range(args.inference_samples)] randomize_position(data_list, args.no_torsion, False, args.tr_sigma_max) predictions_list = None failed_convergence_counter = 0 while predictions_list == None: try: predictions_list, confidences = sampling(data_list=data_list, model=model.module if device.type == 'cuda' else model, inference_steps=args.inference_steps, tr_schedule=tr_schedule, rot_schedule=rot_schedule, tor_schedule=tor_schedule, device=device, t_to_sigma=t_to_sigma, model_args=args, t_schedule=t_schedule) except Exception as e: failed_convergence_counter += 1 if failed_convergence_counter > 5: print('failed 5 times - skipping the complex') break print("Exception while running inference on complex:", e) if failed_convergence_counter > 5: rmsds.extend([100] * args.inference_samples) min_rmsds.append(100) continue if args.no_torsion: orig_complex_graph['ligand'].orig_pos = (orig_complex_graph[ 'ligand'].pos.cpu().numpy() + orig_complex_graph.original_center.cpu().numpy()) filterHs = torch.not_equal(predictions_list[0]['ligand'].x[:, 0], 0).cpu().numpy() if isinstance(orig_complex_graph['ligand'].orig_pos, list): orig_complex_graph['ligand'].orig_pos = orig_complex_graph['ligand'].orig_pos[0] # if len(orig_complex_graph['ligand'].orig_pos.shape) == 3: # orig_complex_graph['ligand'].orig_pos = orig_complex_graph['ligand'].orig_pos[0] ligand_pos = np.asarray( [complex_graph['ligand'].pos.cpu().numpy()[filterHs] for complex_graph in predictions_list]) if len(orig_complex_graph['ligand'].orig_pos.shape) == 2: orig_complex_graph['ligand'].orig_pos = orig_complex_graph['ligand'].orig_pos[None, :, :] try: orig_ligand_pos = orig_complex_graph['ligand'].orig_pos[:, filterHs] - orig_complex_graph.original_center.cpu().numpy() except Exception as e: print("problem with orig_pos which is of shape:", orig_complex_graph['ligand'].orig_pos.shape, e) continue mol = RemoveAllHs(orig_complex_graph.mol[0]) complex_rmsds = [] for i in range(len(orig_ligand_pos)): try: rmsd = get_symmetry_rmsd(mol, orig_ligand_pos[i], [l for l in ligand_pos]) except Exception as e: print("Using non corrected RMSD because of the error:", e) rmsd = np.sqrt(((ligand_pos - orig_ligand_pos[i]) ** 2).sum(axis=2).mean(axis=1)) complex_rmsds.append(rmsd) complex_rmsds = np.asarray(complex_rmsds) rmsd = np.min(complex_rmsds, axis=0) rmsds.extend([r for r in rmsd]) min_rmsds.append(rmsd.min(axis=0)) rmsds = np.array(rmsds) min_rmsds = np.array(min_rmsds) losses = {'rmsds_lt2': (100 * (rmsds < 2).sum() / len(rmsds)), 'rmsds_lt5': (100 * (rmsds < 5).sum() / len(rmsds)), 'min_rmsds_lt2': (100 * (min_rmsds < 2).sum() / len(min_rmsds)), 'min_rmsds_lt5': (100 * (min_rmsds < 5).sum() / len(min_rmsds)),} return losses