def setUp(self):
self.optimizer = OptimizerMock()
self.trainer = Trainer(ModelMock(), CriterionMock(),
self.optimizer, DatasetMock())
self.num_epochs = 3
self.dataset_size = len(self.trainer.dataset)
self.num_iters = self.num_epochs * self.dataset_size
def setUp(self):
self.optimizer = OptimizerMock()
self.trainer = Trainer(ModelMock(), CriterionMock(),
self.optimizer, DatasetMock())
self.num_epochs = 3
self.dataset_size = len(self.trainer.dataset)
self.num_iters = self.num_epochs * self.dataset_size
class TestTrainer(TestCase):
intervals = [
[(1, 'iteration')],
[(1, 'epoch')],
[(1, 'batch')],
[(1, 'update')],
[(5, 'iteration')],
[(5, 'epoch')],
[(5, 'batch')],
[(5, 'update')],
[(1, 'iteration'), (1, 'epoch')],
[(5, 'update'), (1, 'iteration')],
[(2, 'epoch'), (1, 'batch')],
]
def setUp(self):
self.optimizer = OptimizerMock()
self.trainer = Trainer(ModelMock(), CriterionMock(),
self.optimizer, DatasetMock())
self.num_epochs = 3
self.dataset_size = len(self.trainer.dataset)
self.num_iters = self.num_epochs * self.dataset_size
def test_register_plugin(self):
for interval in self.intervals:
simple_plugin = SimplePlugin(interval)
self.trainer.register_plugin(simple_plugin)
self.assertEqual(simple_plugin.trainer, self.trainer)
def test_optimizer_step(self):
self.trainer.run(epochs=1)
self.assertEqual(self.trainer.optimizer.num_steps, 10)
def test_plugin_interval(self):
for interval in self.intervals:
self.setUp()
simple_plugin = SimplePlugin(interval)
self.trainer.register_plugin(simple_plugin)
self.trainer.run(epochs=self.num_epochs)
units = {
('iteration', self.num_iters),
('epoch', self.num_epochs),
('batch', self.num_iters),
('update', self.num_iters)
}
for unit, num_triggers in units:
call_every = None
for i, i_unit in interval:
if i_unit == unit:
call_every = i
break
if call_every:
expected_num_calls = math.floor(num_triggers / call_every)
else:
expected_num_calls = 0
num_calls = getattr(simple_plugin, 'num_' + unit)
self.assertEqual(num_calls, expected_num_calls, 0)
def test_model_called(self):
self.trainer.run(epochs=self.num_epochs)
num_model_calls = self.trainer.model.num_calls
num_crit_calls = self.trainer.criterion.num_calls
self.assertEqual(num_model_calls, num_crit_calls)
for num_calls in [num_model_calls, num_crit_calls]:
lower_bound = OptimizerMock.min_evals * self.num_iters
upper_bound = OptimizerMock.max_evals * self.num_iters
self.assertEqual(num_calls, self.trainer.optimizer.num_evals)
self.assertLessEqual(lower_bound, num_calls)
self.assertLessEqual(num_calls, upper_bound)
def test_model_gradient(self):
self.trainer.run(epochs=self.num_epochs)
output_var = self.trainer.model.output
expected_grad = torch.ones(1, 1) * 2 * self.optimizer.num_evals
self.assertEqual(output_var.grad.data, expected_grad)
class TestTrainer(TestCase):
intervals = [
[(1, 'iteration')],
[(1, 'epoch')],
[(1, 'batch')],
[(1, 'update')],
[(5, 'iteration')],
[(5, 'epoch')],
[(5, 'batch')],
[(5, 'update')],
[(1, 'iteration'), (1, 'epoch')],
[(5, 'update'), (1, 'iteration')],
[(2, 'epoch'), (1, 'batch')],
]
def setUp(self):
self.optimizer = OptimizerMock()
self.trainer = Trainer(ModelMock(), CriterionMock(),
self.optimizer, DatasetMock())
self.num_epochs = 3
self.dataset_size = len(self.trainer.dataset)
self.num_iters = self.num_epochs * self.dataset_size
def test_register_plugin(self):
for interval in self.intervals:
simple_plugin = SimplePlugin(interval)
self.trainer.register_plugin(simple_plugin)
self.assertEqual(simple_plugin.trainer, self.trainer)
def test_optimizer_step(self):
self.trainer.run(epochs=1)
self.assertEqual(self.trainer.optimizer.num_steps, 10)
def test_plugin_interval(self):
for interval in self.intervals:
self.setUp()
simple_plugin = SimplePlugin(interval)
self.trainer.register_plugin(simple_plugin)
self.trainer.run(epochs=self.num_epochs)
units = {
('iteration', self.num_iters),
('epoch', self.num_epochs),
('batch', self.num_iters),
('update', self.num_iters)
}
for unit, num_triggers in units:
call_every = None
for i, i_unit in interval:
if i_unit == unit:
call_every = i
break
if call_every:
expected_num_calls = math.floor(num_triggers / call_every)
else:
expected_num_calls = 0
num_calls = getattr(simple_plugin, 'num_' + unit)
self.assertEqual(num_calls, expected_num_calls, 0)
def test_model_called(self):
self.trainer.run(epochs=self.num_epochs)
num_model_calls = self.trainer.model.num_calls
num_crit_calls = self.trainer.criterion.num_calls
self.assertEqual(num_model_calls, num_crit_calls)
for num_calls in [num_model_calls, num_crit_calls]:
lower_bound = OptimizerMock.min_evals * self.num_iters
upper_bound = OptimizerMock.max_evals * self.num_iters
self.assertEqual(num_calls, self.trainer.optimizer.num_evals)
self.assertLessEqual(lower_bound, num_calls)
self.assertLessEqual(num_calls, upper_bound)
def test_model_gradient(self):
self.trainer.run(epochs=self.num_epochs)
output_var = self.trainer.model.output
expected_grad = torch.ones(1, 1) * 2 * self.optimizer.num_evals
self.assertEqual(output_var.grad.data, expected_grad)
