def train_epoch(logger, loader, model, optimizer, scheduler):
model.train()
time_start = time.time()
for batch in loader:
optimizer.zero_grad()
batch.to(torch.device(cfg.device))
pred, true = model(batch)
loss_ret = compute_loss(pred, true, batch)
if len(loss_ret) == 2:
total_loss, pred_score = loss_ret
loss_main = torch.tensor(0)
loss_reg = torch.tensor(0)
else:
total_loss, pred_score, loss_main, loss_reg = loss_ret
total_loss.backward()
optimizer.step()
logger.update_stats(true=true.detach().cpu(),
pred=pred_score.detach().cpu(),
loss=total_loss.item(),
lr=scheduler.get_last_lr()[0],
time_used=time.time() - time_start,
params=cfg.params,
loss_main=loss_main.item(),
loss_reg=loss_reg.item())
time_start = time.time()
scheduler.step()
def eval_epoch(logger, loader, model):
model.eval()
time_start = time.time()
for batch in loader:
batch.to(torch.device(cfg.device))
pred, true = model(batch)
loss, pred_score = compute_loss(pred, true)
logger.update_stats(true=true.detach().cpu(),
pred=pred_score.detach().cpu(),
loss=loss.item(),
lr=0,
time_used=time.time() - time_start,
params=cfg.params)
time_start = time.time()
def train_epoch(logger, loader, model, optimizer, scheduler):
model.train()
time_start = time.time()
for batch in loader:
optimizer.zero_grad()
batch.to(torch.device(cfg.device))
pred, true = model(batch)
loss, pred_score = compute_loss(pred, true)
loss.backward()
optimizer.step()
logger.update_stats(true=true.detach().cpu(),
pred=pred_score.detach().cpu(),
loss=loss.item(),
lr=scheduler.get_last_lr()[0],
time_used=time.time() - time_start,
params=cfg.params)
time_start = time.time()
scheduler.step()
def eval_epoch(logger, loader, model):
model.eval()
time_start = time.time()
for batch in loader:
batch.to(torch.device(cfg.device))
pred, true = model(batch)
loss_ret = compute_loss(pred, true, batch)
if len(loss_ret) == 2: # todo duplicate code: unpacking of loss values
total_loss, pred_score = loss_ret
loss_main = None
loss_reg = None
else:
total_loss, pred_score, loss_main, loss_reg = loss_ret
logger.update_stats(true=true.detach().cpu(),
pred=pred_score.detach().cpu(),
loss=total_loss.item(),
lr=0,
time_used=time.time() - time_start,
params=cfg.params,
loss_main=loss_main.item(),
loss_reg=loss_reg.item())
time_start = time.time()