epoch + 1, lr, train_res['loss'], train_res['accuracy'],
test_res['loss'], test_res['accuracy'], time_ep, train_res['nll'],
train_res['kl'], test_res['accuracy'], test_res['loss']
]
table = tabulate.tabulate([values],
columns,
tablefmt='simple',
floatfmt='8.4f')
if epoch % 40 == 0:
table = table.split('\n')
table = '\n'.join([table[1]] + table)
else:
table = table.split('\n')[2]
print(table)
print('Final test res:', test_res)
test_preds, test_targets = utils.predictions(loaders['test'], model)
pth = os.path.join(args.dir, args.fname + '.pt')
print('Save to:', pth)
torch.save(model.state_dict(), pth)
print('Global time: ', time.time() - global_time)
model.eval()
print('\nEnsemble results:')
for ns in [1, 10, 100]:
acc, llh, log_prob, targets, preds = utils.ens_pred(
model, loaders['test'], ns)
print('ns %s: acc %.4f, llh %.4f' % (ns, acc, llh))
print('\n\n', args.dataset, args.model, args.beta, acc, llh, '\n')
for epoch in range(start_epoch, args.epochs):
time_ep = time.time()
lr = schedule(epoch)
utils.adjust_learning_rate(optimizer, lr)
model.train()
train_res = utils.train_epoch(loaders['train'], model, criterion, optimizer)
test_res = {'loss': None, 'accuracy': None}
valid_res = {'loss': None, 'accuracy': None}
tta_res = {'loss': None, 'accuracy': None}
if (epoch + 1) % args.eval_freq == 0:
with torch.no_grad():
model.eval()
# Testing
log_prob, targets = utils.predictions(loaders['test'], model)
metrics_ts = metrics_kfold(log_prob, targets, n_splits=2, n_runs=5, temp_scale=True)
test_res['loss'] = -metrics_ts['ll']
test_res['accuracy'] = metrics_ts['acc']
# Validation
if args.valid_size > 0:
log_prob, targets = utils.predictions(loaders['valid'], model)
metrics_ts = metrics_kfold(log_prob, targets, n_splits=2, n_runs=5, temp_scale=True)
valid_res['loss'] = -metrics_ts['ll']
valid_res['accuracy'] = metrics_ts['acc']
# Test-time augmentation
log_probs = []
for i in range(args.num_tta):
log_prob, targets = utils.predictions(loaders['tta'], model)
log_probs.append(log_prob)
log_prob = logsumexp(np.dstack(log_probs), axis=2) - np.log(args.num_tta)