def train(model: nn.Module,
data: DataLoader,
loss_func: Callable,
optimizer: Optimizer,
scheduler: NoamLR,
args: Namespace,
n_iter: int = 0,
logger: logging.Logger = None,
writer: SummaryWriter = None) -> int:
"""
Trains a model for an epoch.
:param model: Model.
:param data: A DataLoader.
:param loss_func: Loss function.
:param optimizer: Optimizer.
:param scheduler: A NoamLR learning rate scheduler.
:param args: Arguments.
:param n_iter: The number of iterations (training examples) trained on so far.
:param logger: A logger for printing intermediate results.
:param writer: A tensorboardX SummaryWriter.
:return: The total number of iterations (training examples) trained on so far.
"""
debug = logger.debug if logger is not None else print
model.train()
loss_sum, iter_count = 0, 0
for batch in tqdm(data, total=len(data)):
if args.cuda:
targets = batch.y.float().unsqueeze(1).cuda()
else:
targets = batch.y.float().unsqueeze(1)
batch = GlassBatchMolGraph(
batch) # TODO: Apply a check for connectivity of graph
# Run model
model.zero_grad()
preds = model(batch)
loss = loss_func(preds, targets)
loss = loss.sum() / loss.size(0)
loss_sum += loss.item()
iter_count += len(batch)
loss.backward()
if args.max_grad_norm is not None:
clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step()
n_iter += len(batch)
# Log and/or add to tensorboard
if (n_iter // args.batch_size) % args.log_frequency == 0:
lr = scheduler.get_lr()[0]
pnorm = compute_pnorm(model)
gnorm = compute_gnorm(model)
loss_avg = loss_sum / iter_count
loss_sum, iter_count = 0, 0
debug("Loss = {:.4e}, PNorm = {:.4f}, GNorm = {:.4f}, lr = {:.4e}".
format(loss_avg, pnorm, gnorm, lr))
if writer is not None:
writer.add_scalar('train_loss', loss_avg, n_iter)
writer.add_scalar('param_norm', pnorm, n_iter)
writer.add_scalar('gradient_norm', gnorm, n_iter)
writer.add_scalar('learning_rate', lr, n_iter)
return n_iter
class GAN(nn.Module):
def __init__(self, args: Namespace, prediction_model: nn.Module,
encoder: nn.Module):
super(GAN, self).__init__()
self.args = args
self.prediction_model = prediction_model
self.encoder = encoder
self.hidden_size = args.hidden_size
self.disc_input_size = args.hidden_size + args.output_size
self.act_func = self.encoder.encoder.act_func
self.netD = nn.Sequential(
nn.Linear(self.disc_input_size, self.hidden_size
), # doesn't support jtnn or additional features rn
self.act_func,
nn.Linear(self.hidden_size, self.hidden_size),
self.act_func,
nn.Linear(self.hidden_size, self.hidden_size),
self.act_func,
nn.Linear(self.hidden_size, 1))
self.beta = args.wgan_beta
# the optimizers don't really belong here, but we put it here so that we don't clutter code for other opts
self.optimizerG = Adam(self.encoder.parameters(),
lr=args.init_lr[0] * args.gan_lr_mult,
betas=(0, 0.9))
self.optimizerD = Adam(self.netD.parameters(),
lr=args.init_lr[0] * args.gan_lr_mult,
betas=(0, 0.9))
self.use_scheduler = args.gan_use_scheduler
if self.use_scheduler:
self.schedulerG = NoamLR(
self.optimizerG,
warmup_epochs=args.warmup_epochs,
total_epochs=args.epochs,
steps_per_epoch=args.train_data_length // args.batch_size,
init_lr=args.init_lr[0] * args.gan_lr_mult,
max_lr=args.max_lr[0] * args.gan_lr_mult,
final_lr=args.final_lr[0] * args.gan_lr_mult)
self.schedulerD = NoamLR(
self.optimizerD,
warmup_epochs=args.warmup_epochs,
total_epochs=args.epochs,
steps_per_epoch=(args.train_data_length // args.batch_size) *
args.gan_d_per_g,
init_lr=args.init_lr[0] * args.gan_lr_mult,
max_lr=args.max_lr[0] * args.gan_lr_mult,
final_lr=args.final_lr[0] * args.gan_lr_mult)
def forward(self, smiles_batch: List[str], features=None) -> torch.Tensor:
return self.prediction_model(smiles_batch, features)
# TODO maybe this isn't the best way to wrap the MOE class, but it works for now
def mahalanobis_metric(self, p, mu, j):
return self.prediction_model.mahalanobis_metric(p, mu, j)
def compute_domain_encs(self, all_train_smiles):
return self.prediction_model.compute_domain_encs(all_train_smiles)
def compute_minibatch_domain_encs(self, train_smiles):
return self.prediction_model.compute_minibatch_domain_encs(
train_smiles)
def compute_loss(self, train_smiles, train_targets, test_smiles):
return self.prediction_model.compute_loss(train_smiles, train_targets,
test_smiles)
def set_domain_encs(self, domain_encs):
self.prediction_model.domain_encs = domain_encs
def get_domain_encs(self):
return self.prediction_model.domain_encs
# the following methods are code borrowed from Wengong and modified
def train_D(self, fake_smiles: List[str], real_smiles: List[str]):
self.netD.zero_grad()
real_output = self.prediction_model(real_smiles).detach()
real_enc_output = self.encoder.saved_encoder_output.detach()
real_vecs = torch.cat([real_enc_output, real_output], dim=1)
fake_output = self.prediction_model(fake_smiles).detach()
fake_enc_output = self.encoder.saved_encoder_output.detach()
fake_vecs = torch.cat([fake_enc_output, fake_output], dim=1)
# real_vecs = self.encoder(mol2graph(real_smiles, self.args)).detach()
# fake_vecs = self.encoder(mol2graph(fake_smiles, self.args)).detach()
real_score = self.netD(real_vecs)
fake_score = self.netD(fake_vecs)
score = fake_score.mean() - real_score.mean(
) #maximize -> minimize minus
score.backward()
#Gradient Penalty
inter_gp, inter_norm = self.gradient_penalty(real_vecs, fake_vecs)
inter_gp.backward()
self.optimizerD.step()
if self.use_scheduler:
self.schedulerD.step()
return -score.item(), inter_norm
def train_G(self, fake_smiles: List[str], real_smiles: List[str]):
self.encoder.zero_grad()
real_output = self.prediction_model(real_smiles).detach()
real_enc_output = self.encoder.saved_encoder_output
real_vecs = torch.cat([real_enc_output, real_output], dim=1)
fake_output = self.prediction_model(fake_smiles).detach()
fake_enc_output = self.encoder.saved_encoder_output
fake_vecs = torch.cat([fake_enc_output, fake_output], dim=1)
# real_vecs = self.encoder(mol2graph(real_smiles, self.args))
# fake_vecs = self.encoder(mol2graph(fake_smiles, self.args))
real_score = self.netD(real_vecs)
fake_score = self.netD(fake_vecs)
score = real_score.mean() - fake_score.mean()
score.backward()
self.optimizerG.step()
if self.use_scheduler:
self.schedulerG.step()
self.netD.zero_grad(
) #technically not necessary since it'll get zero'd in the next iteration anyway
return score.item()
def gradient_penalty(self, real_vecs, fake_vecs):
assert real_vecs.size() == fake_vecs.size()
eps = torch.rand(real_vecs.size(0), 1).cuda()
inter_data = eps * real_vecs + (1 - eps) * fake_vecs
inter_data = autograd.Variable(
inter_data, requires_grad=True
) # TODO check if this is necessary (we detached earlier)
inter_score = self.netD(inter_data)
inter_score = inter_score.view(-1) # bs*hidden
inter_grad = autograd.grad(inter_score,
inter_data,
grad_outputs=torch.ones(
inter_score.size()).cuda(),
create_graph=True,
retain_graph=True,
only_inputs=True)[0]
inter_norm = inter_grad.norm(2, dim=1)
inter_gp = ((inter_norm - 1)**2).mean() * self.beta
return inter_gp, inter_norm.mean().item()