File size: 18,376 Bytes
ce9690f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 | 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
|