def test(self):
epoch = self.load()
self.test_logger = get_logger('test.{}.{}'.format(
self.__class__.__name__, self.tag))
acc_meter = AverageMeter('test_accuracy')
for samples, labels in tqdm(self.dataset.test_loader(self.batch_size),
desc='Test'):
with tc.no_grad():
guids, image, bow = samples
preds = self.model(image, bow)
acc = self.metric(preds, labels)
acc_meter.add(acc.item(), labels.size(0))
_, top = preds.topk(self.metric.top_k, dim=1)
match = labels.unsqueeze(dim=1).eq(top).any(dim=1, keepdim=False)
for i, eq in enumerate(match):
if not bool(eq.item()):
guid = guids[i]
label = self.dataset.labels.inverse_transform(
labels[i].item())
preds = self.dataset.labels.inverse_transform(
top[i].cpu().numpy())
self.test_logger.info(
'Prediction failure: {} belongs to {}, but was predicted as {}'
.format(guid, label, preds))
self.test_logger.info(
'After {} epochs of training, {} = {:.4f}'.format(
epoch + 1, acc_meter.tag, acc_meter.read()))
def train(self, num_epochs, resume=False):
if resume:
start_epoch = self.load()
else:
start_epoch = 0
self.train_logger.info('-' * 100)
timer = TimeMeter()
tm = AverageMeter('train_loss')
for epoch in range(start_epoch, start_epoch + num_epochs):
tm.reset()
for samples, labels in tqdm(
self.dataset.train_loader(self.batch_size,
self.num_training_samples),
desc='Train epoch {}'.format(epoch + 1)):
_, image, bow = samples
preds = self.model(image, bow)
loss = self.loss_func(preds, labels)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
tm.add(loss.item(), labels.size(0))
vm_loss, vm_acc = self.eval()
if self.decayer.is_better(vm_acc.read(), self.decayer.best):
self.dump(epoch)
self.decayer.step(vm_acc.read())
self.train_logger.info(
'Epoch {:02d}, elapsed Time {:.2f}, {} = {:.4f}, {} = {:.4f}, {} = {:.4f}'
.format(epoch + 1, timer.read(), tm.tag,
tm.read(), vm_loss.tag, vm_loss.read(), vm_acc.tag,
vm_acc.read()))
def eval(self):
loss_meter = AverageMeter('valid_loss')
acc_meter = AverageMeter('valid_accuracy')
for samples, labels in tqdm(self.dataset.valid_loader(self.batch_size),
desc='Validation'):
with tc.no_grad():
_, image, bow = samples
preds = self.model(image, bow)
loss = self.loss_func(preds, labels)
loss_meter.add(loss.item(), labels.size(0))
acc = self.metric(preds, labels)
acc_meter.add(acc.item(), labels.size(0))
return loss_meter, acc_meter