def test_resume_from_multiple_of_epoches(self, mock_evaluator, mock_checkpoint, mock_func):
mock_model = mock.Mock()
mock_optim = mock.Mock()
trainer = SupervisedTrainer(batch_size=16)
trainer.optimizer = mock_optim
n_epoches = 1
start_epoch = 1
step = 7
trainer._train_epoches(self.dataset, mock_model, n_epoches, start_epoch, step, dev_data=self.dataset)
def test_batch_num_when_resuming(self, mock_evaluator, mock_checkpoint, mock_func):
mock_model = mock.Mock()
mock_optim = mock.Mock()
trainer = SupervisedTrainer(batch_size=16)
trainer.optimizer = mock_optim
n_epoches = 1
start_epoch = 1
steps_per_epoch = 7
step = 3
trainer._train_epoches(self.dataset, mock_model, n_epoches, start_epoch, step)
self.assertEqual(steps_per_epoch - step, mock_func.call_count)
def test_resume_from_multiple_of_epoches(self, mock_evaluator, mock_checkpoint, mock_func):
mock_optim = mock.Mock()
trainer = SupervisedTrainer()
trainer.model = mock.Mock()
trainer.optimizer = mock.Mock()
callbacks = CallbackContainer(trainer)
n_epoches = 1
start_epoch = 1
step = 7
trainer.set_local_parameters(123, [], [], [], 1, 1)
trainer._train_epoches(
self.data_iterator, n_epoches, start_epoch, step, callbacks, self.data_iterator)
def test_batch_num_when_resuming(self, mock_evaluator, mock_checkpoint, mock_func):
trainer = SupervisedTrainer()
trainer.model = mock.Mock()
trainer.optimizer = mock.Mock()
callbacks = CallbackContainer(trainer)
n_epoches = 1
start_epoch = 1
steps_per_epoch = len(self.data_iterator)
step = 3
trainer.set_local_parameters(123, [], [], [], 1, 1)
trainer._train_epoches(self.data_iterator, n_epoches,
start_epoch, step, callbacks, self.data_iterator)
self.assertEqual(steps_per_epoch - step, mock_func.call_count)
return_value=[])
@mock.patch('machine.util.checkpoint.Checkpoint.save')
@mock.patch('machine.evaluator.Evaluator.evaluate', return_value=([], []))
def test_batch_num_when_resuming(
self, mock_evaluator, mock_checkpoint, mock_func):
>>>>>>> upstream/master
mock_model = mock.Mock()
mock_optim = mock.Mock()
trainer = SupervisedTrainer(batch_size=16)
trainer.optimizer = mock_optim
n_epoches = 1
start_epoch = 1
steps_per_epoch = 7
step = 3
trainer._train_epoches(self.dataset, mock_model,
n_epoches, start_epoch, step)
self.assertEqual(steps_per_epoch - step, mock_func.call_count)
@mock.patch('machine.trainer.SupervisedTrainer._train_batch',
return_value=0)
@mock.patch('machine.util.checkpoint.Checkpoint.save')
@mock.patch('machine.evaluator.Evaluator.evaluate', return_value=([], []))
def test_resume_from_multiple_of_epoches(
self, mock_evaluator, mock_checkpoint, mock_func):
mock_model = mock.Mock()
mock_optim = mock.Mock()
trainer = SupervisedTrainer(batch_size=16)
trainer.optimizer = mock_optim
n_epoches = 1
start_epoch = 1
