def train(model, loader, criterion, optimizer, epoch, device, opt):
model.train()
train_loss = 0.0
losses = AverageMeter()
metric = Metric(opt.num_classes)
for i, (imgs, spatial_locations, word_vectors, targets_predicates,
targets_confidences) in enumerate(loader):
# compute outputs
imgs, spatial_locations, word_vectors, targets_confidences, targets_predicates = imgs.to(
device), spatial_locations.to(device), word_vectors.to(
device), targets_confidences.to(device), targets_predicates.to(
device)
confidences, predicates = model(imgs, spatial_locations, word_vectors)
# compute loss
loss1 = criterion(confidences, targets_confidences)
loss2 = criterion(predicates, targets_predicates)
tot_loss = loss1 + loss2
train_loss += tot_loss.item()
losses.update(tot_loss.item(), imgs.size(0))
predicates = torch.sigmoid(predicates)
metric.update(predicates, targets_predicates)
optimizer.zero_grad()
tot_loss.backward()
optimizer.step()
# show information
if (i + 1) % opt.log_interval == 0:
avg_loss = train_loss / opt.log_interval
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, losses.count, len(loader.dataset),
100. * (i + 1) / len(loader), avg_loss))
train_loss = 0.0
# show information
recall = metric.compute_metrics()
print('Train set ({:d} samples): Average loss: {:.4f}\tRecall: {:.4f}'.
format(losses.count, losses.avg, recall))
return losses.avg, recall
def validate(model, loader, criterion, epoch, device, opt):
model.eval()
losses = AverageMeter()
metric = Metric(opt.num_classes)
with torch.no_grad():
for i, (imgs, spatial_locations, word_vectors, targets_predicates, targets_confidences) in enumerate(loader):
# compute outputs
imgs, spatial_locations, word_vectors, targets_confidences, targets_predicates = imgs.to(device), spatial_locations.to(
device), word_vectors.to(device), targets_confidences.to(device), targets_predicates.to(device)
confidences, predicates = model(imgs, spatial_locations, word_vectors)
# compute loss
loss = criterion(predicates, targets_predicates)
metric.update(predicates, targets_predicates)
losses.update(loss.item(), imgs.size(0))
# show information
recall = metric.compute_metrics()
print('Validation set ({:d} samples): Average loss: {:.4f}\tRecall: {:.4f}'.format(losses.count, losses.avg, recall))
return losses.avg, recall