def test_step_result_obj(self, batch, batch_idx, *args, **kwargs):
"""
Default, baseline test_step
:param batch:
:return:
"""
x, y = batch
x = x.view(x.size(0), -1)
y_hat = self(x)
loss_test = self.loss(y, y_hat)
# acc
labels_hat = torch.argmax(y_hat, dim=1)
test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
test_acc = torch.tensor(test_acc)
test_acc = test_acc.type_as(x)
result = EvalResult()
# alternate possible outputs to test
if batch_idx % 1 == 0:
result.log_dict({'test_loss': loss_test, 'test_acc': test_acc})
return result
if batch_idx % 2 == 0:
return test_acc
if batch_idx % 3 == 0:
result.log_dict({'test_loss': loss_test, 'test_acc': test_acc})
result.test_dic = {'test_loss_a': loss_test}
return result
def test_step(self, batch, batch_idx):
logits = self(batch, model='best')
labels = batch[3]
loss = F.cross_entropy(logits, labels, reduction='mean')
result = EvalResult()
result.log('test_loss', loss, sync_dist=True)
result.log_dict(self.calculate_metrics(logits, labels, prefix='test'),
sync_dist=True)
return result
def validation_step(self, batch, batch_idx):
logits = self(batch)
labels = batch[3]
loss = F.cross_entropy(logits, labels, reduction='mean')
result = EvalResult(early_stop_on=loss, checkpoint_on=loss)
result.log('val_loss', loss, sync_dist=True)
result.log_dict(self.calculate_metrics(logits, labels, prefix='val'),
sync_dist=True)
return result
def validation_step_result_obj(self, batch, batch_idx, *args, **kwargs):
x, y = batch
x = x.view(x.size(0), -1)
y_hat = self(x)
loss_val = self.loss(y, y_hat)
# acc
labels_hat = torch.argmax(y_hat, dim=1)
val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
val_acc = torch.tensor(val_acc).type_as(x)
result = EvalResult(checkpoint_on=loss_val, early_stop_on=loss_val)
result.log_dict({
'val_loss': loss_val,
'val_acc': val_acc,
})
return result
