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import numpy as np
from tqdm import tqdm
import torch
import gc
from torch.distributions import Normal
from loguru import logger
# from training import AverageMeter
# from mdn_utils import mdn_loss_fn
def mdn_loss_fn(pi, sigma, mu, y,dist_threhold=7.0,eps = 1e-10):
mu = torch.clip(torch.nan_to_num(mu,0.0),min=1e-6)
sigma = torch.clip(torch.nan_to_num(sigma,0.0),min=1e-6)
pi = torch.clip(torch.nan_to_num(pi,0.0),min=1e-6)
"""calculate the mdn """
normal = Normal(mu.real, sigma.real)
loglik = normal.log_prob(y.expand_as(normal.loc))
loss = -torch.logsumexp(torch.log(pi.real + eps) + loglik, dim=1)
loss = loss[torch.where(y <= dist_threhold)[0]]
loss = loss.mean()
return torch.nan_to_num(loss,0.0)
# def mdn_loss_fn(pi, sigma, mu, y, eps=1e-10):
# normal = Normal(mu, sigma)
# #loss = th.exp(normal.log_prob(y.expand_as(normal.loc)))
# #loss = th.sum(loss * pi, dim=1)
# #loss = -th.log(loss)
# loglik = normal.log_prob(y.expand_as(normal.loc))
# loss = -th.logsumexp(th.log(pi + eps) + loglik, dim=1)
# return loss
import torch as th
def mdn_loss_fn_min_diatance_atom(pi, sigma, mu, y,dist_threhold=7.0,eps = 1e-10,topN = 1):
mu = torch.clip(torch.nan_to_num(mu,0.0),min=1e-6)
sigma = torch.clip(torch.nan_to_num(sigma,0.0),min=1e-6)
pi = torch.clip(torch.nan_to_num(pi,0.0),min=1e-6)
"""use ca- pose to calculate the mdn """
normal = Normal(mu.real, sigma.real)
loglik = normal.log_prob(y.expand_as(normal.loc))
loss = -torch.logsumexp(torch.log(pi.real + eps) + loglik, dim=1)
loss = loss[torch.where(y <= dist_threhold)[0]]
loss = loss.mean()
return loss
def calculate_probablity(pi, sigma, mu, y,dist_threhold=5.0,eps = 1e-10):
mu = torch.clip(torch.nan_to_num(mu,0.0),min=1e-6)
sigma = torch.clip(torch.nan_to_num(sigma,0.0),min=1e-6)
pi = torch.clip(torch.nan_to_num(pi,0.0),min=1e-6)
normal = Normal(mu.real, sigma.real)
logprob = normal.log_prob(y.expand_as(normal.loc))
logprob += torch.log(pi.real + eps )
prob = logprob.exp().sum(1)
prob[torch.where(y > dist_threhold)[0]] = 0.
return prob
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() if self.unpooled_metrics else v
else:
for type_idx, v in enumerate(vals):
# logger.info(interval_idx[type_idx])
# logger.info(v)
# logger.info(interval_idx[type_idx], torch.ones(len(v)))
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_mdn_epoch(model, loader, optimizer, device,accelerator,ema_weights):
model.train()
meter = AverageMeter(['loss','mdn_loss_interaction', 'mdn_loss_ligand', 'atom_types_loss', 'bond_types_loss', 'residue_types_loss'],
unpooled_metrics=True)
for data in loader:
# for data in tqdm(loader, total=len(loader),disable=not accelerator.is_local_main_process):
if device.type == 'cuda' and len(data) == 1 or device.type == 'cpu' and data.num_graphs == 1:
logger.info("Skipping batch of size 1 since otherwise batchnorm would not work.")
optimizer.zero_grad()
try:
# pi, sigma, mu, dist = model(data)
with accelerator.autocast():
mdn_loss_interaction , mdn_loss_ligand , atom_types_loss , bond_types_loss , residue_types_loss = model(data)#mdn_loss_fn(pi, sigma, mu, dist)
# logger.info(loss)
loss = mdn_loss_interaction + mdn_loss_ligand + 0.001*atom_types_loss + 0.001*bond_types_loss + 0.001*residue_types_loss
accelerator.backward(loss)
optimizer.step()
# gather all loss for plot
mdn_loss_interaction= accelerator.gather(mdn_loss_interaction)
mdn_loss_ligand= accelerator.gather(mdn_loss_ligand)
atom_types_loss= accelerator.gather(atom_types_loss)
bond_types_loss= accelerator.gather(bond_types_loss)
residue_types_loss= accelerator.gather(residue_types_loss)
loss= accelerator.gather(loss)
# logger.info('loss val: ',loss.mean().cpu().detach())
metrics = [loss.mean().cpu().detach(),mdn_loss_interaction.mean().cpu().detach() , mdn_loss_ligand.mean().cpu().detach() , atom_types_loss.mean().cpu().detach() , bond_types_loss.mean().cpu().detach() , residue_types_loss.mean().cpu().detach()]
meter.add(metrics)
# logger.info('loss train: ',loss.mean().cpu().detach())
ema_weights.update(model.parameters())
# meter.add([loss.mean().cpu().detach()])
except RuntimeError as e:
if 'out of memory' in str(e):
logger.info('| WARNING: ran out of memory, skipping batch')
for p in model.parameters():
if p.grad is not None:
del p.grad # free some memory
optimizer.zero_grad()
del data
# loss = 0.0*sum([p.sum() for p in model.parameters() if p.requires_grad])
# accelerator.backward(loss)
# optimizer.step()
gc.collect()
torch.cuda.empty_cache()
continue
elif 'Input mismatch' in str(e):
logger.info('| WARNING: weird torch_cluster error, skipping batch')
for p in model.parameters():
if p.grad is not None:
del p.grad # free some memory
optimizer.zero_grad()
del data
gc.collect()
torch.cuda.empty_cache()
continue
else:
raise e
return meter.summary()
def test_mdn_epoch(model, loader, device,accelerator, test_sigma_intervals=False):
model.eval()
meter = AverageMeter(['loss','mdn_loss_interaction', 'mdn_loss_ligand', 'atom_types_loss', 'bond_types_loss', 'residue_types_loss'],
unpooled_metrics=True)
if test_sigma_intervals:
meter_all = AverageMeter(
['loss'],
unpooled_metrics=True, intervals=10)
for data in loader:
try:
with torch.no_grad():
# pi, sigma, mu, dist,_ = model(data)
with accelerator.autocast():
mdn_loss_interaction , mdn_loss_ligand , atom_types_loss , bond_types_loss , residue_types_loss,_ = model(data)#mdn_loss_fn(pi, sigma, mu, dist)
loss = mdn_loss_interaction + mdn_loss_ligand + 0.001*atom_types_loss + 0.001*bond_types_loss + 0.001*residue_types_loss
mdn_loss_interaction= accelerator.gather(mdn_loss_interaction)
mdn_loss_ligand= accelerator.gather(mdn_loss_ligand)
atom_types_loss= accelerator.gather(atom_types_loss)
bond_types_loss= accelerator.gather(bond_types_loss)
residue_types_loss= accelerator.gather(residue_types_loss)
loss= accelerator.gather(loss)
# logger.info('loss val: ',loss.mean().cpu().detach())
metrics = [loss.mean().cpu().detach(),mdn_loss_interaction.mean().cpu().detach() , mdn_loss_ligand.mean().cpu().detach() , atom_types_loss.mean().cpu().detach() , bond_types_loss.mean().cpu().detach() , residue_types_loss.mean().cpu().detach()]
meter.add(metrics)
except RuntimeError as e:
if 'out of memory' in str(e):
logger.info('| WARNING: ran out of memory, skipping batch')
for p in model.parameters():
if p.grad is not None:
del p.grad # free some memory
del data
gc.collect()
torch.cuda.empty_cache()
continue
elif 'Input mismatch' in str(e):
logger.info('| WARNING: weird torch_cluster error, skipping batch')
for p in model.parameters():
if p.grad is not None:
del p.grad # free some memory
del data
gc.collect()
torch.cuda.empty_cache()
continue
else:
raise e
out = meter.summary()
# if test_sigma_intervals > 0: out.update(meter_all.summary())
return out |