logits = model(images)
loss = F.cross_entropy(logits, labels)
val_accs.append((logits.argmax(1) == labels).float().mean().item())
val_losses.append(loss.item())
if CALLBACK:
CALLBACK.scalar('Val_Loss', epoch + 1, val_losses.last_avg)
CALLBACK.scalar('Val_Acc', epoch + 1, val_accs.last_avg)
print('Epoch {} | Validation | Loss: {:.4f}, Accs: {:.4f}'.format(epoch, val_losses.avg, val_accs.avg))
if not (epoch + 1) % args.save_freq:
save_checkpoint(
model.state_dict(),
os.path.join(args.save_folder, args.save_name + 'acc{}_{}.pth'.format(val_accs.avg, (epoch + 1))), cpu=True)
valacc_final = val_accs.avg
test_accs = AverageMeter()
test_losses = AverageMeter()
with torch.no_grad():
for i, batch_data in enumerate(tqdm.tqdm(test_loader, ncols=80)):
images, labels = batch_data['image'].to(DEVICE), batch_data['label_idx'].to(DEVICE)
logits = model(images)
loss = F.cross_entropy(logits, labels)
test_accs.append((logits.argmax(1) == labels).float().mean().item())
test_losses.append(loss.item())
for i, (images, labels) in enumerate(tqdm.tqdm(val_loader, ncols=80)):
images, labels = images.to(DEVICE), labels.to(DEVICE)
logits = model(images)
loss = F.cross_entropy(logits, labels)
val_accs.append((logits.argmax(1) == labels).float().mean().item())
val_losses.append(loss.item())
print('Epoch {} | Validation | Loss: {:.4f}, Accs: {:.4f}'.format(
epoch, val_losses.avg, val_accs.avg))
if val_accs.avg >= best_acc:
best_acc = val_accs.avg
best_epoch = epoch
best_dict = model.state_dict()
if not (epoch + 1) % args.save_freq:
save_checkpoint(model.state_dict(),
os.path.join(
args.save_folder,
args.save_name + '_{}.pth'.format(epoch + 1)),
cpu=True)
model.load_state_dict(best_dict)
test_accs = AverageMeter()
test_losses = AverageMeter()
with torch.no_grad():
for i, (images, labels) in enumerate(tqdm.tqdm(test_loader, ncols=80)):